Merge branch 'master' into retrofit-gas-pipelines
This commit is contained in:
Fabian Neumann
GitHub
commit
37e2e53486
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results
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694
LICENSE.txt
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GNU GENERAL PUBLIC LICENSE
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MIT License
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Version 3, 29 June 2007
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Copyright 2017-2021 The PyPSA-Eur Authors
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|
||||||
doubtful cases shall be resolved in favor of coverage. For a particular
|
|
||||||
product received by a particular user, "normally used" refers to a
|
|
||||||
typical or common use of that class of product, regardless of the status
|
|
||||||
of the particular user or of the way in which the particular user
|
|
||||||
actually uses, or expects or is expected to use, the product. A product
|
|
||||||
is a consumer product regardless of whether the product has substantial
|
|
||||||
commercial, industrial or non-consumer uses, unless such uses represent
|
|
||||||
the only significant mode of use of the product.
|
|
||||||
|
|
||||||
"Installation Information" for a User Product means any methods,
|
|
||||||
procedures, authorization keys, or other information required to install
|
|
||||||
and execute modified versions of a covered work in that User Product from
|
|
||||||
a modified version of its Corresponding Source. The information must
|
|
||||||
suffice to ensure that the continued functioning of the modified object
|
|
||||||
code is in no case prevented or interfered with solely because
|
|
||||||
modification has been made.
|
|
||||||
|
|
||||||
If you convey an object code work under this section in, or with, or
|
|
||||||
specifically for use in, a User Product, and the conveying occurs as
|
|
||||||
part of a transaction in which the right of possession and use of the
|
|
||||||
User Product is transferred to the recipient in perpetuity or for a
|
|
||||||
fixed term (regardless of how the transaction is characterized), the
|
|
||||||
Corresponding Source conveyed under this section must be accompanied
|
|
||||||
by the Installation Information. But this requirement does not apply
|
|
||||||
if neither you nor any third party retains the ability to install
|
|
||||||
modified object code on the User Product (for example, the work has
|
|
||||||
been installed in ROM).
|
|
||||||
|
|
||||||
The requirement to provide Installation Information does not include a
|
|
||||||
requirement to continue to provide support service, warranty, or updates
|
|
||||||
for a work that has been modified or installed by the recipient, or for
|
|
||||||
the User Product in which it has been modified or installed. Access to a
|
|
||||||
network may be denied when the modification itself materially and
|
|
||||||
adversely affects the operation of the network or violates the rules and
|
|
||||||
protocols for communication across the network.
|
|
||||||
|
|
||||||
Corresponding Source conveyed, and Installation Information provided,
|
|
||||||
in accord with this section must be in a format that is publicly
|
|
||||||
documented (and with an implementation available to the public in
|
|
||||||
source code form), and must require no special password or key for
|
|
||||||
unpacking, reading or copying.
|
|
||||||
|
|
||||||
7. Additional Terms.
|
|
||||||
|
|
||||||
"Additional permissions" are terms that supplement the terms of this
|
|
||||||
License by making exceptions from one or more of its conditions.
|
|
||||||
Additional permissions that are applicable to the entire Program shall
|
|
||||||
be treated as though they were included in this License, to the extent
|
|
||||||
that they are valid under applicable law. If additional permissions
|
|
||||||
apply only to part of the Program, that part may be used separately
|
|
||||||
under those permissions, but the entire Program remains governed by
|
|
||||||
this License without regard to the additional permissions.
|
|
||||||
|
|
||||||
When you convey a copy of a covered work, you may at your option
|
|
||||||
remove any additional permissions from that copy, or from any part of
|
|
||||||
it. (Additional permissions may be written to require their own
|
|
||||||
removal in certain cases when you modify the work.) You may place
|
|
||||||
additional permissions on material, added by you to a covered work,
|
|
||||||
for which you have or can give appropriate copyright permission.
|
|
||||||
|
|
||||||
Notwithstanding any other provision of this License, for material you
|
|
||||||
add to a covered work, you may (if authorized by the copyright holders of
|
|
||||||
that material) supplement the terms of this License with terms:
|
|
||||||
|
|
||||||
a) Disclaiming warranty or limiting liability differently from the
|
|
||||||
terms of sections 15 and 16 of this License; or
|
|
||||||
|
|
||||||
b) Requiring preservation of specified reasonable legal notices or
|
|
||||||
author attributions in that material or in the Appropriate Legal
|
|
||||||
Notices displayed by works containing it; or
|
|
||||||
|
|
||||||
c) Prohibiting misrepresentation of the origin of that material, or
|
|
||||||
requiring that modified versions of such material be marked in
|
|
||||||
reasonable ways as different from the original version; or
|
|
||||||
|
|
||||||
d) Limiting the use for publicity purposes of names of licensors or
|
|
||||||
authors of the material; or
|
|
||||||
|
|
||||||
e) Declining to grant rights under trademark law for use of some
|
|
||||||
trade names, trademarks, or service marks; or
|
|
||||||
|
|
||||||
f) Requiring indemnification of licensors and authors of that
|
|
||||||
material by anyone who conveys the material (or modified versions of
|
|
||||||
it) with contractual assumptions of liability to the recipient, for
|
|
||||||
any liability that these contractual assumptions directly impose on
|
|
||||||
those licensors and authors.
|
|
||||||
|
|
||||||
All other non-permissive additional terms are considered "further
|
|
||||||
restrictions" within the meaning of section 10. If the Program as you
|
|
||||||
received it, or any part of it, contains a notice stating that it is
|
|
||||||
governed by this License along with a term that is a further
|
|
||||||
restriction, you may remove that term. If a license document contains
|
|
||||||
a further restriction but permits relicensing or conveying under this
|
|
||||||
License, you may add to a covered work material governed by the terms
|
|
||||||
of that license document, provided that the further restriction does
|
|
||||||
not survive such relicensing or conveying.
|
|
||||||
|
|
||||||
If you add terms to a covered work in accord with this section, you
|
|
||||||
must place, in the relevant source files, a statement of the
|
|
||||||
additional terms that apply to those files, or a notice indicating
|
|
||||||
where to find the applicable terms.
|
|
||||||
|
|
||||||
Additional terms, permissive or non-permissive, may be stated in the
|
|
||||||
form of a separately written license, or stated as exceptions;
|
|
||||||
the above requirements apply either way.
|
|
||||||
|
|
||||||
8. Termination.
|
|
||||||
|
|
||||||
You may not propagate or modify a covered work except as expressly
|
|
||||||
provided under this License. Any attempt otherwise to propagate or
|
|
||||||
modify it is void, and will automatically terminate your rights under
|
|
||||||
this License (including any patent licenses granted under the third
|
|
||||||
paragraph of section 11).
|
|
||||||
|
|
||||||
However, if you cease all violation of this License, then your
|
|
||||||
license from a particular copyright holder is reinstated (a)
|
|
||||||
provisionally, unless and until the copyright holder explicitly and
|
|
||||||
finally terminates your license, and (b) permanently, if the copyright
|
|
||||||
holder fails to notify you of the violation by some reasonable means
|
|
||||||
prior to 60 days after the cessation.
|
|
||||||
|
|
||||||
Moreover, your license from a particular copyright holder is
|
|
||||||
reinstated permanently if the copyright holder notifies you of the
|
|
||||||
violation by some reasonable means, this is the first time you have
|
|
||||||
received notice of violation of this License (for any work) from that
|
|
||||||
copyright holder, and you cure the violation prior to 30 days after
|
|
||||||
your receipt of the notice.
|
|
||||||
|
|
||||||
Termination of your rights under this section does not terminate the
|
|
||||||
licenses of parties who have received copies or rights from you under
|
|
||||||
this License. If your rights have been terminated and not permanently
|
|
||||||
reinstated, you do not qualify to receive new licenses for the same
|
|
||||||
material under section 10.
|
|
||||||
|
|
||||||
9. Acceptance Not Required for Having Copies.
|
|
||||||
|
|
||||||
You are not required to accept this License in order to receive or
|
|
||||||
run a copy of the Program. Ancillary propagation of a covered work
|
|
||||||
occurring solely as a consequence of using peer-to-peer transmission
|
|
||||||
to receive a copy likewise does not require acceptance. However,
|
|
||||||
nothing other than this License grants you permission to propagate or
|
|
||||||
modify any covered work. These actions infringe copyright if you do
|
|
||||||
not accept this License. Therefore, by modifying or propagating a
|
|
||||||
covered work, you indicate your acceptance of this License to do so.
|
|
||||||
|
|
||||||
10. Automatic Licensing of Downstream Recipients.
|
|
||||||
|
|
||||||
Each time you convey a covered work, the recipient automatically
|
|
||||||
receives a license from the original licensors, to run, modify and
|
|
||||||
propagate that work, subject to this License. You are not responsible
|
|
||||||
for enforcing compliance by third parties with this License.
|
|
||||||
|
|
||||||
An "entity transaction" is a transaction transferring control of an
|
|
||||||
organization, or substantially all assets of one, or subdividing an
|
|
||||||
organization, or merging organizations. If propagation of a covered
|
|
||||||
work results from an entity transaction, each party to that
|
|
||||||
transaction who receives a copy of the work also receives whatever
|
|
||||||
licenses to the work the party's predecessor in interest had or could
|
|
||||||
give under the previous paragraph, plus a right to possession of the
|
|
||||||
Corresponding Source of the work from the predecessor in interest, if
|
|
||||||
the predecessor has it or can get it with reasonable efforts.
|
|
||||||
|
|
||||||
You may not impose any further restrictions on the exercise of the
|
|
||||||
rights granted or affirmed under this License. For example, you may
|
|
||||||
not impose a license fee, royalty, or other charge for exercise of
|
|
||||||
rights granted under this License, and you may not initiate litigation
|
|
||||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
|
||||||
any patent claim is infringed by making, using, selling, offering for
|
|
||||||
sale, or importing the Program or any portion of it.
|
|
||||||
|
|
||||||
11. Patents.
|
|
||||||
|
|
||||||
A "contributor" is a copyright holder who authorizes use under this
|
|
||||||
License of the Program or a work on which the Program is based. The
|
|
||||||
work thus licensed is called the contributor's "contributor version".
|
|
||||||
|
|
||||||
A contributor's "essential patent claims" are all patent claims
|
|
||||||
owned or controlled by the contributor, whether already acquired or
|
|
||||||
hereafter acquired, that would be infringed by some manner, permitted
|
|
||||||
by this License, of making, using, or selling its contributor version,
|
|
||||||
but do not include claims that would be infringed only as a
|
|
||||||
consequence of further modification of the contributor version. For
|
|
||||||
purposes of this definition, "control" includes the right to grant
|
|
||||||
patent sublicenses in a manner consistent with the requirements of
|
|
||||||
this License.
|
|
||||||
|
|
||||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
|
||||||
patent license under the contributor's essential patent claims, to
|
|
||||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
|
||||||
propagate the contents of its contributor version.
|
|
||||||
|
|
||||||
In the following three paragraphs, a "patent license" is any express
|
|
||||||
agreement or commitment, however denominated, not to enforce a patent
|
|
||||||
(such as an express permission to practice a patent or covenant not to
|
|
||||||
sue for patent infringement). To "grant" such a patent license to a
|
|
||||||
party means to make such an agreement or commitment not to enforce a
|
|
||||||
patent against the party.
|
|
||||||
|
|
||||||
If you convey a covered work, knowingly relying on a patent license,
|
|
||||||
and the Corresponding Source of the work is not available for anyone
|
|
||||||
to copy, free of charge and under the terms of this License, through a
|
|
||||||
publicly available network server or other readily accessible means,
|
|
||||||
then you must either (1) cause the Corresponding Source to be so
|
|
||||||
available, or (2) arrange to deprive yourself of the benefit of the
|
|
||||||
patent license for this particular work, or (3) arrange, in a manner
|
|
||||||
consistent with the requirements of this License, to extend the patent
|
|
||||||
license to downstream recipients. "Knowingly relying" means you have
|
|
||||||
actual knowledge that, but for the patent license, your conveying the
|
|
||||||
covered work in a country, or your recipient's use of the covered work
|
|
||||||
in a country, would infringe one or more identifiable patents in that
|
|
||||||
country that you have reason to believe are valid.
|
|
||||||
|
|
||||||
If, pursuant to or in connection with a single transaction or
|
|
||||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
|
||||||
covered work, and grant a patent license to some of the parties
|
|
||||||
receiving the covered work authorizing them to use, propagate, modify
|
|
||||||
or convey a specific copy of the covered work, then the patent license
|
|
||||||
you grant is automatically extended to all recipients of the covered
|
|
||||||
work and works based on it.
|
|
||||||
|
|
||||||
A patent license is "discriminatory" if it does not include within
|
|
||||||
the scope of its coverage, prohibits the exercise of, or is
|
|
||||||
conditioned on the non-exercise of one or more of the rights that are
|
|
||||||
specifically granted under this License. You may not convey a covered
|
|
||||||
work if you are a party to an arrangement with a third party that is
|
|
||||||
in the business of distributing software, under which you make payment
|
|
||||||
to the third party based on the extent of your activity of conveying
|
|
||||||
the work, and under which the third party grants, to any of the
|
|
||||||
parties who would receive the covered work from you, a discriminatory
|
|
||||||
patent license (a) in connection with copies of the covered work
|
|
||||||
conveyed by you (or copies made from those copies), or (b) primarily
|
|
||||||
for and in connection with specific products or compilations that
|
|
||||||
contain the covered work, unless you entered into that arrangement,
|
|
||||||
or that patent license was granted, prior to 28 March 2007.
|
|
||||||
|
|
||||||
Nothing in this License shall be construed as excluding or limiting
|
|
||||||
any implied license or other defenses to infringement that may
|
|
||||||
otherwise be available to you under applicable patent law.
|
|
||||||
|
|
||||||
12. No Surrender of Others' Freedom.
|
|
||||||
|
|
||||||
If conditions are imposed on you (whether by court order, agreement or
|
|
||||||
otherwise) that contradict the conditions of this License, they do not
|
|
||||||
excuse you from the conditions of this License. If you cannot convey a
|
|
||||||
covered work so as to satisfy simultaneously your obligations under this
|
|
||||||
License and any other pertinent obligations, then as a consequence you may
|
|
||||||
not convey it at all. For example, if you agree to terms that obligate you
|
|
||||||
to collect a royalty for further conveying from those to whom you convey
|
|
||||||
the Program, the only way you could satisfy both those terms and this
|
|
||||||
License would be to refrain entirely from conveying the Program.
|
|
||||||
|
|
||||||
13. Use with the GNU Affero General Public License.
|
|
||||||
|
|
||||||
Notwithstanding any other provision of this License, you have
|
|
||||||
permission to link or combine any covered work with a work licensed
|
|
||||||
under version 3 of the GNU Affero General Public License into a single
|
|
||||||
combined work, and to convey the resulting work. The terms of this
|
|
||||||
License will continue to apply to the part which is the covered work,
|
|
||||||
but the special requirements of the GNU Affero General Public License,
|
|
||||||
section 13, concerning interaction through a network will apply to the
|
|
||||||
combination as such.
|
|
||||||
|
|
||||||
14. Revised Versions of this License.
|
|
||||||
|
|
||||||
The Free Software Foundation may publish revised and/or new versions of
|
|
||||||
the GNU General Public License from time to time. Such new versions will
|
|
||||||
be similar in spirit to the present version, but may differ in detail to
|
|
||||||
address new problems or concerns.
|
|
||||||
|
|
||||||
Each version is given a distinguishing version number. If the
|
|
||||||
Program specifies that a certain numbered version of the GNU General
|
|
||||||
Public License "or any later version" applies to it, you have the
|
|
||||||
option of following the terms and conditions either of that numbered
|
|
||||||
version or of any later version published by the Free Software
|
|
||||||
Foundation. If the Program does not specify a version number of the
|
|
||||||
GNU General Public License, you may choose any version ever published
|
|
||||||
by the Free Software Foundation.
|
|
||||||
|
|
||||||
If the Program specifies that a proxy can decide which future
|
|
||||||
versions of the GNU General Public License can be used, that proxy's
|
|
||||||
public statement of acceptance of a version permanently authorizes you
|
|
||||||
to choose that version for the Program.
|
|
||||||
|
|
||||||
Later license versions may give you additional or different
|
|
||||||
permissions. However, no additional obligations are imposed on any
|
|
||||||
author or copyright holder as a result of your choosing to follow a
|
|
||||||
later version.
|
|
||||||
|
|
||||||
15. Disclaimer of Warranty.
|
|
||||||
|
|
||||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
|
||||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
|
||||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
|
||||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
|
||||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
|
||||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
|
||||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
|
||||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
|
||||||
|
|
||||||
16. Limitation of Liability.
|
|
||||||
|
|
||||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
|
||||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
|
||||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
|
||||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
|
||||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
|
||||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
|
||||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
|
||||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
|
||||||
SUCH DAMAGES.
|
|
||||||
|
|
||||||
17. Interpretation of Sections 15 and 16.
|
|
||||||
|
|
||||||
If the disclaimer of warranty and limitation of liability provided
|
|
||||||
above cannot be given local legal effect according to their terms,
|
|
||||||
reviewing courts shall apply local law that most closely approximates
|
|
||||||
an absolute waiver of all civil liability in connection with the
|
|
||||||
Program, unless a warranty or assumption of liability accompanies a
|
|
||||||
copy of the Program in return for a fee.
|
|
||||||
|
|
||||||
END OF TERMS AND CONDITIONS
|
|
||||||
|
|
||||||
How to Apply These Terms to Your New Programs
|
|
||||||
|
|
||||||
If you develop a new program, and you want it to be of the greatest
|
|
||||||
possible use to the public, the best way to achieve this is to make it
|
|
||||||
free software which everyone can redistribute and change under these terms.
|
|
||||||
|
|
||||||
To do so, attach the following notices to the program. It is safest
|
|
||||||
to attach them to the start of each source file to most effectively
|
|
||||||
state the exclusion of warranty; and each file should have at least
|
|
||||||
the "copyright" line and a pointer to where the full notice is found.
|
|
||||||
|
|
||||||
{one line to give the program's name and a brief idea of what it does.}
|
|
||||||
Copyright (C) {year} {name of author}
|
|
||||||
|
|
||||||
This program is free software: you can redistribute it and/or modify
|
|
||||||
it under the terms of the GNU General Public License as published by
|
|
||||||
the Free Software Foundation, either version 3 of the License, or
|
|
||||||
(at your option) any later version.
|
|
||||||
|
|
||||||
This program is distributed in the hope that it will be useful,
|
|
||||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
||||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
||||||
GNU General Public License for more details.
|
|
||||||
|
|
||||||
You should have received a copy of the GNU General Public License
|
|
||||||
along with this program. If not, see <http://www.gnu.org/licenses/>.
|
|
||||||
|
|
||||||
Also add information on how to contact you by electronic and paper mail.
|
|
||||||
|
|
||||||
If the program does terminal interaction, make it output a short
|
|
||||||
notice like this when it starts in an interactive mode:
|
|
||||||
|
|
||||||
{project} Copyright (C) {year} {fullname}
|
|
||||||
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
|
||||||
This is free software, and you are welcome to redistribute it
|
|
||||||
under certain conditions; type `show c' for details.
|
|
||||||
|
|
||||||
The hypothetical commands `show w' and `show c' should show the appropriate
|
|
||||||
parts of the General Public License. Of course, your program's commands
|
|
||||||
might be different; for a GUI interface, you would use an "about box".
|
|
||||||
|
|
||||||
You should also get your employer (if you work as a programmer) or school,
|
|
||||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
|
||||||
For more information on this, and how to apply and follow the GNU GPL, see
|
|
||||||
<http://www.gnu.org/licenses/>.
|
|
||||||
|
|
||||||
The GNU General Public License does not permit incorporating your program
|
|
||||||
into proprietary programs. If your program is a subroutine library, you
|
|
||||||
may consider it more useful to permit linking proprietary applications with
|
|
||||||
the library. If this is what you want to do, use the GNU Lesser General
|
|
||||||
Public License instead of this License. But first, please read
|
|
||||||
<http://www.gnu.org/philosophy/why-not-lgpl.html>.
|
|
||||||
25
README.md
25
README.md
@ -9,21 +9,24 @@
|
|||||||
|
|
||||||
|
|
||||||
|
|
||||||
**WARNING**: This model is under construction and contains serious
|
**WARNING**: This model is under construction and contains serious problems that
|
||||||
problems that distort the results. See the github repository
|
distort the results. See the github repository
|
||||||
[issues](https://github.com/PyPSA/pypsa-eur-sec/issues) for some of
|
[issues](https://github.com/PyPSA/pypsa-eur-sec/issues) for some of the problems
|
||||||
the problems (please feel free to help or make suggestions). There is
|
(please feel free to help or make suggestions). There is neither a full
|
||||||
neither documentation nor a paper yet, but we hope to have a preprint
|
documentation nor a paper yet, but we hope to have a preprint out by the end of 2021.
|
||||||
out by autumn 2021. We cannot support this model if you choose to use
|
You can find out more about the model capabilities in [a recent
|
||||||
it.
|
presentation at EMP-E](https://nworbmot.org/energy/brown-empe.pdf) or the
|
||||||
|
following [preprint with a description of the industry
|
||||||
|
sector](https://arxiv.org/abs/2109.09563). We cannot support this model if you
|
||||||
|
choose to use it.
|
||||||
|
|
||||||
|
|
||||||
PyPSA-Eur-Sec builds on the electricity generation and transmission
|
PyPSA-Eur-Sec builds on the electricity generation and transmission
|
||||||
model [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur) to add demand
|
model [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur) to add demand
|
||||||
and supply for the following sectors: transport, space and water
|
and supply for the following sectors: transport, space and water
|
||||||
heating, biomass, industry and industrial feedstocks. This completes
|
heating, biomass, industry and industrial feedstocks, agriculture,
|
||||||
the energy system and includes all greenhouse gas emitters except
|
forestry and fishing. This completes the energy system and includes
|
||||||
waste management, agriculture, forestry and land use.
|
all greenhouse gas emitters except waste management and land use.
|
||||||
|
|
||||||
Please see the [documentation](https://pypsa-eur-sec.readthedocs.io/)
|
Please see the [documentation](https://pypsa-eur-sec.readthedocs.io/)
|
||||||
for installation instructions and other useful information about the snakemake workflow.
|
for installation instructions and other useful information about the snakemake workflow.
|
||||||
@ -65,6 +68,6 @@ the additional sectors.
|
|||||||
# Licence
|
# Licence
|
||||||
|
|
||||||
The code in PyPSA-Eur-Sec is released as free software under the
|
The code in PyPSA-Eur-Sec is released as free software under the
|
||||||
[GPLv3](http://www.gnu.org/licenses/gpl-3.0.en.html), see LICENSE.txt.
|
[MIT License](https://opensource.org/licenses/MIT), see `LICENSE.txt`.
|
||||||
However, different licenses and terms of use may apply to the various
|
However, different licenses and terms of use may apply to the various
|
||||||
input data.
|
input data.
|
||||||
|
|||||||
58
Snakefile
58
Snakefile
@ -1,4 +1,7 @@
|
|||||||
|
|
||||||
|
from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider
|
||||||
|
HTTP = HTTPRemoteProvider()
|
||||||
|
|
||||||
configfile: "config.yaml"
|
configfile: "config.yaml"
|
||||||
|
|
||||||
|
|
||||||
@ -6,7 +9,6 @@ wildcard_constraints:
|
|||||||
lv="[a-z0-9\.]+",
|
lv="[a-z0-9\.]+",
|
||||||
simpl="[a-zA-Z0-9]*",
|
simpl="[a-zA-Z0-9]*",
|
||||||
clusters="[0-9]+m?",
|
clusters="[0-9]+m?",
|
||||||
sectors="[+a-zA-Z0-9]+",
|
|
||||||
opts="[-+a-zA-Z0-9]*",
|
opts="[-+a-zA-Z0-9]*",
|
||||||
sector_opts="[-+a-zA-Z0-9\.\s]*"
|
sector_opts="[-+a-zA-Z0-9\.\s]*"
|
||||||
|
|
||||||
@ -21,7 +23,6 @@ subworkflow pypsaeur:
|
|||||||
snakefile: "../pypsa-eur/Snakefile"
|
snakefile: "../pypsa-eur/Snakefile"
|
||||||
configfile: "../pypsa-eur/config.yaml"
|
configfile: "../pypsa-eur/config.yaml"
|
||||||
|
|
||||||
|
|
||||||
rule all:
|
rule all:
|
||||||
input: SDIR + '/graphs/costs.pdf'
|
input: SDIR + '/graphs/costs.pdf'
|
||||||
|
|
||||||
@ -167,6 +168,7 @@ rule build_energy_totals:
|
|||||||
co2="data/eea/UNFCCC_v23.csv",
|
co2="data/eea/UNFCCC_v23.csv",
|
||||||
swiss="data/switzerland-sfoe/switzerland-new_format.csv",
|
swiss="data/switzerland-sfoe/switzerland-new_format.csv",
|
||||||
idees="data/jrc-idees-2015",
|
idees="data/jrc-idees-2015",
|
||||||
|
district_heat_share='data/district_heat_share.csv',
|
||||||
eurostat=input_eurostat
|
eurostat=input_eurostat
|
||||||
output:
|
output:
|
||||||
energy_name='resources/energy_totals.csv',
|
energy_name='resources/energy_totals.csv',
|
||||||
@ -180,16 +182,37 @@ rule build_energy_totals:
|
|||||||
|
|
||||||
rule build_biomass_potentials:
|
rule build_biomass_potentials:
|
||||||
input:
|
input:
|
||||||
jrc_potentials="data/biomass/JRC Biomass Potentials.xlsx"
|
enspreso_biomass=HTTP.remote("https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx", keep_local=True),
|
||||||
|
nuts2="data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson", # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
|
||||||
|
regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
|
||||||
|
nuts3_population="../pypsa-eur/data/bundle/nama_10r_3popgdp.tsv.gz",
|
||||||
|
swiss_cantons="../pypsa-eur/data/bundle/ch_cantons.csv",
|
||||||
|
swiss_population="../pypsa-eur/data/bundle/je-e-21.03.02.xls",
|
||||||
|
country_shapes=pypsaeur('resources/country_shapes.geojson')
|
||||||
output:
|
output:
|
||||||
biomass_potentials_all='resources/biomass_potentials_all.csv',
|
biomass_potentials_all='resources/biomass_potentials_all_s{simpl}_{clusters}.csv',
|
||||||
biomass_potentials='resources/biomass_potentials.csv'
|
biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv'
|
||||||
threads: 1
|
threads: 1
|
||||||
resources: mem_mb=1000
|
resources: mem_mb=1000
|
||||||
benchmark: "benchmarks/build_biomass_potentials"
|
benchmark: "benchmarks/build_biomass_potentials_s{simpl}_{clusters}"
|
||||||
script: 'scripts/build_biomass_potentials.py'
|
script: 'scripts/build_biomass_potentials.py'
|
||||||
|
|
||||||
|
|
||||||
|
if config["sector"]["biomass_transport"]:
|
||||||
|
rule build_biomass_transport_costs:
|
||||||
|
input:
|
||||||
|
transport_cost_data=HTTP.remote("publications.jrc.ec.europa.eu/repository/bitstream/JRC98626/biomass potentials in europe_web rev.pdf", keep_local=True)
|
||||||
|
output:
|
||||||
|
biomass_transport_costs="resources/biomass_transport_costs.csv",
|
||||||
|
threads: 1
|
||||||
|
resources: mem_mb=1000
|
||||||
|
benchmark: "benchmarks/build_biomass_transport_costs"
|
||||||
|
script: 'scripts/build_biomass_transport_costs.py'
|
||||||
|
build_biomass_transport_costs_output = rules.build_biomass_transport_costs.output
|
||||||
|
else:
|
||||||
|
build_biomass_transport_costs_output = {}
|
||||||
|
|
||||||
|
|
||||||
rule build_ammonia_production:
|
rule build_ammonia_production:
|
||||||
input:
|
input:
|
||||||
usgs="data/myb1-2017-nitro.xls"
|
usgs="data/myb1-2017-nitro.xls"
|
||||||
@ -230,10 +253,10 @@ rule build_industrial_production_per_country_tomorrow:
|
|||||||
input:
|
input:
|
||||||
industrial_production_per_country="resources/industrial_production_per_country.csv"
|
industrial_production_per_country="resources/industrial_production_per_country.csv"
|
||||||
output:
|
output:
|
||||||
industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow.csv"
|
industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow_{planning_horizons}.csv"
|
||||||
threads: 1
|
threads: 1
|
||||||
resources: mem_mb=1000
|
resources: mem_mb=1000
|
||||||
benchmark: "benchmarks/build_industrial_production_per_country_tomorrow"
|
benchmark: "benchmarks/build_industrial_production_per_country_tomorrow_{planning_horizons}"
|
||||||
script: 'scripts/build_industrial_production_per_country_tomorrow.py'
|
script: 'scripts/build_industrial_production_per_country_tomorrow.py'
|
||||||
|
|
||||||
|
|
||||||
@ -253,25 +276,25 @@ rule build_industrial_distribution_key:
|
|||||||
rule build_industrial_production_per_node:
|
rule build_industrial_production_per_node:
|
||||||
input:
|
input:
|
||||||
industrial_distribution_key="resources/industrial_distribution_key_elec_s{simpl}_{clusters}.csv",
|
industrial_distribution_key="resources/industrial_distribution_key_elec_s{simpl}_{clusters}.csv",
|
||||||
industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow.csv"
|
industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow_{planning_horizons}.csv"
|
||||||
output:
|
output:
|
||||||
industrial_production_per_node="resources/industrial_production_elec_s{simpl}_{clusters}.csv"
|
industrial_production_per_node="resources/industrial_production_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
|
||||||
threads: 1
|
threads: 1
|
||||||
resources: mem_mb=1000
|
resources: mem_mb=1000
|
||||||
benchmark: "benchmarks/build_industrial_production_per_node/s{simpl}_{clusters}"
|
benchmark: "benchmarks/build_industrial_production_per_node/s{simpl}_{clusters}_{planning_horizons}"
|
||||||
script: 'scripts/build_industrial_production_per_node.py'
|
script: 'scripts/build_industrial_production_per_node.py'
|
||||||
|
|
||||||
|
|
||||||
rule build_industrial_energy_demand_per_node:
|
rule build_industrial_energy_demand_per_node:
|
||||||
input:
|
input:
|
||||||
industry_sector_ratios="resources/industry_sector_ratios.csv",
|
industry_sector_ratios="resources/industry_sector_ratios.csv",
|
||||||
industrial_production_per_node="resources/industrial_production_elec_s{simpl}_{clusters}.csv",
|
industrial_production_per_node="resources/industrial_production_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
|
||||||
industrial_energy_demand_per_node_today="resources/industrial_energy_demand_today_elec_s{simpl}_{clusters}.csv"
|
industrial_energy_demand_per_node_today="resources/industrial_energy_demand_today_elec_s{simpl}_{clusters}.csv"
|
||||||
output:
|
output:
|
||||||
industrial_energy_demand_per_node="resources/industrial_energy_demand_elec_s{simpl}_{clusters}.csv"
|
industrial_energy_demand_per_node="resources/industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
|
||||||
threads: 1
|
threads: 1
|
||||||
resources: mem_mb=1000
|
resources: mem_mb=1000
|
||||||
benchmark: "benchmarks/build_industrial_energy_demand_per_node/s{simpl}_{clusters}"
|
benchmark: "benchmarks/build_industrial_energy_demand_per_node/s{simpl}_{clusters}_{planning_horizons}"
|
||||||
script: 'scripts/build_industrial_energy_demand_per_node.py'
|
script: 'scripts/build_industrial_energy_demand_per_node.py'
|
||||||
|
|
||||||
|
|
||||||
@ -334,7 +357,7 @@ rule prepare_sector_network:
|
|||||||
clustered_gas_network="resources/gas_network_elec_s{simpl}_{clusters}.csv",
|
clustered_gas_network="resources/gas_network_elec_s{simpl}_{clusters}.csv",
|
||||||
traffic_data_KFZ="data/emobility/KFZ__count",
|
traffic_data_KFZ="data/emobility/KFZ__count",
|
||||||
traffic_data_Pkw="data/emobility/Pkw__count",
|
traffic_data_Pkw="data/emobility/Pkw__count",
|
||||||
biomass_potentials='resources/biomass_potentials.csv',
|
biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv',
|
||||||
heat_profile="data/heat_load_profile_BDEW.csv",
|
heat_profile="data/heat_load_profile_BDEW.csv",
|
||||||
costs=CDIR + "costs_{planning_horizons}.csv",
|
costs=CDIR + "costs_{planning_horizons}.csv",
|
||||||
profile_offwind_ac=pypsaeur("resources/profile_offwind-ac.nc"),
|
profile_offwind_ac=pypsaeur("resources/profile_offwind-ac.nc"),
|
||||||
@ -344,7 +367,7 @@ rule prepare_sector_network:
|
|||||||
busmap=pypsaeur("resources/busmap_elec_s{simpl}_{clusters}.csv"),
|
busmap=pypsaeur("resources/busmap_elec_s{simpl}_{clusters}.csv"),
|
||||||
clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
|
clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
|
||||||
simplified_pop_layout="resources/pop_layout_elec_s{simpl}.csv",
|
simplified_pop_layout="resources/pop_layout_elec_s{simpl}.csv",
|
||||||
industrial_demand="resources/industrial_energy_demand_elec_s{simpl}_{clusters}.csv",
|
industrial_demand="resources/industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
|
||||||
heat_demand_urban="resources/heat_demand_urban_elec_s{simpl}_{clusters}.nc",
|
heat_demand_urban="resources/heat_demand_urban_elec_s{simpl}_{clusters}.nc",
|
||||||
heat_demand_rural="resources/heat_demand_rural_elec_s{simpl}_{clusters}.nc",
|
heat_demand_rural="resources/heat_demand_rural_elec_s{simpl}_{clusters}.nc",
|
||||||
heat_demand_total="resources/heat_demand_total_elec_s{simpl}_{clusters}.nc",
|
heat_demand_total="resources/heat_demand_total_elec_s{simpl}_{clusters}.nc",
|
||||||
@ -363,7 +386,8 @@ rule prepare_sector_network:
|
|||||||
solar_thermal_total="resources/solar_thermal_total_elec_s{simpl}_{clusters}.nc",
|
solar_thermal_total="resources/solar_thermal_total_elec_s{simpl}_{clusters}.nc",
|
||||||
solar_thermal_urban="resources/solar_thermal_urban_elec_s{simpl}_{clusters}.nc",
|
solar_thermal_urban="resources/solar_thermal_urban_elec_s{simpl}_{clusters}.nc",
|
||||||
solar_thermal_rural="resources/solar_thermal_rural_elec_s{simpl}_{clusters}.nc",
|
solar_thermal_rural="resources/solar_thermal_rural_elec_s{simpl}_{clusters}.nc",
|
||||||
**build_retro_cost_output
|
**build_retro_cost_output,
|
||||||
|
**build_biomass_transport_costs_output
|
||||||
output: RDIR + '/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc'
|
output: RDIR + '/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc'
|
||||||
threads: 1
|
threads: 1
|
||||||
resources: mem_mb=2000
|
resources: mem_mb=2000
|
||||||
|
|||||||
@ -1,4 +1,4 @@
|
|||||||
version: 0.5.0
|
version: 0.6.0
|
||||||
|
|
||||||
logging_level: INFO
|
logging_level: INFO
|
||||||
|
|
||||||
@ -21,15 +21,17 @@ scenario:
|
|||||||
opts: # only relevant for PyPSA-Eur
|
opts: # only relevant for PyPSA-Eur
|
||||||
- ''
|
- ''
|
||||||
sector_opts: # this is where the main scenario settings are
|
sector_opts: # this is where the main scenario settings are
|
||||||
- Co2L0-3H-T-H-B-I-solar+p3-dist1
|
- Co2L0-3H-T-H-B-I-A-solar+p3-dist1
|
||||||
# to really understand the options here, look in scripts/prepare_sector_network.py
|
# to really understand the options here, look in scripts/prepare_sector_network.py
|
||||||
# Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
|
# Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
|
||||||
# Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
|
# Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
|
||||||
# xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
|
# xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
|
||||||
# single letters are sectors: T for land transport, H for building heating,
|
# single letters are sectors: T for land transport, H for building heating,
|
||||||
# B for biomass supply, I for industry, shipping and aviation
|
# B for biomass supply, I for industry, shipping and aviation,
|
||||||
|
# A for agriculture, forestry and fishing
|
||||||
# solar+c0.5 reduces the capital cost of solar to 50\% of reference value
|
# solar+c0.5 reduces the capital cost of solar to 50\% of reference value
|
||||||
# solar+p3 multiplies the available installable potential by factor 3
|
# solar+p3 multiplies the available installable potential by factor 3
|
||||||
|
# co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
|
||||||
# dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
|
# dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
|
||||||
# for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
|
# for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
|
||||||
# emissions throughout the transition path in the timeframe determined by the
|
# emissions throughout the transition path in the timeframe determined by the
|
||||||
@ -71,7 +73,8 @@ electricity:
|
|||||||
|
|
||||||
# regulate what components with which carriers are kept from PyPSA-Eur;
|
# regulate what components with which carriers are kept from PyPSA-Eur;
|
||||||
# some technologies are removed because they are implemented differently
|
# some technologies are removed because they are implemented differently
|
||||||
# or have different year-dependent costs in PyPSA-Eur-Sec
|
# (e.g. battery or H2 storage) or have different year-dependent costs
|
||||||
|
# in PyPSA-Eur-Sec
|
||||||
pypsa_eur:
|
pypsa_eur:
|
||||||
Bus:
|
Bus:
|
||||||
- AC
|
- AC
|
||||||
@ -97,28 +100,28 @@ energy:
|
|||||||
|
|
||||||
biomass:
|
biomass:
|
||||||
year: 2030
|
year: 2030
|
||||||
scenario: Med
|
scenario: ENS_Med
|
||||||
classes:
|
classes:
|
||||||
solid biomass:
|
solid biomass:
|
||||||
- Primary agricultural residues
|
- Agricultural waste
|
||||||
- Forestry energy residue
|
- Fuelwood residues
|
||||||
- Secondary forestry residues
|
- Secondary Forestry residues - woodchips
|
||||||
- Secondary Forestry residues sawdust
|
- Sawdust
|
||||||
- Forestry residues from landscape care biomass
|
- Residues from landscape care
|
||||||
- Municipal waste
|
- Municipal waste
|
||||||
not included:
|
not included:
|
||||||
- Bioethanol sugar beet biomass
|
- Sugar from sugar beet
|
||||||
- Rapeseeds for biodiesel
|
- Rape seed
|
||||||
- sunflower and soya for Biodiesel
|
- "Sunflower, soya seed "
|
||||||
- Starchy crops biomass
|
- Bioethanol barley, wheat, grain maize, oats, other cereals and rye
|
||||||
- Grassy crops biomass
|
- Miscanthus, switchgrass, RCG
|
||||||
- Willow biomass
|
- Willow
|
||||||
- Poplar biomass potential
|
- Poplar
|
||||||
- Roundwood fuelwood
|
- FuelwoodRW
|
||||||
- Roundwood Chips & Pellets
|
- C&P_RW
|
||||||
biogas:
|
biogas:
|
||||||
- Manure biomass potential
|
- Manure solid, liquid
|
||||||
- Sludge biomass
|
- Sludge
|
||||||
|
|
||||||
|
|
||||||
solar_thermal:
|
solar_thermal:
|
||||||
@ -139,8 +142,16 @@ existing_capacities:
|
|||||||
|
|
||||||
|
|
||||||
sector:
|
sector:
|
||||||
central: true
|
district_heating:
|
||||||
central_fraction: 0.6
|
potential: 0.6 # maximum fraction of urban demand which can be supplied by district heating
|
||||||
|
# increase of today's district heating demand to potential maximum district heating share
|
||||||
|
# progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
|
||||||
|
progress: 1
|
||||||
|
# 2020: 0.0
|
||||||
|
# 2030: 0.3
|
||||||
|
# 2040: 0.6
|
||||||
|
# 2050: 1.0
|
||||||
|
district_heating_loss: 0.15
|
||||||
bev_dsm_restriction_value: 0.75 #Set to 0 for no restriction on BEV DSM
|
bev_dsm_restriction_value: 0.75 #Set to 0 for no restriction on BEV DSM
|
||||||
bev_dsm_restriction_time: 7 #Time at which SOC of BEV has to be dsm_restriction_value
|
bev_dsm_restriction_time: 7 #Time at which SOC of BEV has to be dsm_restriction_value
|
||||||
transport_heating_deadband_upper: 20.
|
transport_heating_deadband_upper: 20.
|
||||||
@ -149,7 +160,6 @@ sector:
|
|||||||
ICE_upper_degree_factor: 1.6
|
ICE_upper_degree_factor: 1.6
|
||||||
EV_lower_degree_factor: 0.98
|
EV_lower_degree_factor: 0.98
|
||||||
EV_upper_degree_factor: 0.63
|
EV_upper_degree_factor: 0.63
|
||||||
district_heating_loss: 0.15
|
|
||||||
bev_dsm: true #turns on EV battery
|
bev_dsm: true #turns on EV battery
|
||||||
bev_availability: 0.5 #How many cars do smart charging
|
bev_availability: 0.5 #How many cars do smart charging
|
||||||
bev_energy: 0.05 #average battery size in MWh
|
bev_energy: 0.05 #average battery size in MWh
|
||||||
@ -160,34 +170,46 @@ sector:
|
|||||||
bev_avail_mean: 0.8
|
bev_avail_mean: 0.8
|
||||||
v2g: true #allows feed-in to grid from EV battery
|
v2g: true #allows feed-in to grid from EV battery
|
||||||
#what is not EV or FCEV is oil-fuelled ICE
|
#what is not EV or FCEV is oil-fuelled ICE
|
||||||
land_transport_fuel_cell_share: # 1 means all FCEVs
|
land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
|
||||||
2020: 0
|
# 2020: 0
|
||||||
2030: 0.05
|
# 2030: 0.05
|
||||||
2040: 0.1
|
# 2040: 0.1
|
||||||
2050: 0.15
|
# 2050: 0.15
|
||||||
land_transport_electric_share: # 1 means all EVs
|
land_transport_electric_share: 0.85 # 1 means all EVs
|
||||||
2020: 0
|
# 2020: 0
|
||||||
2030: 0.25
|
# 2030: 0.25
|
||||||
2040: 0.6
|
# 2040: 0.6
|
||||||
2050: 0.85
|
# 2050: 0.85
|
||||||
transport_fuel_cell_efficiency: 0.5
|
transport_fuel_cell_efficiency: 0.5
|
||||||
transport_internal_combustion_efficiency: 0.3
|
transport_internal_combustion_efficiency: 0.3
|
||||||
|
agriculture_machinery_electric_share: 0
|
||||||
|
agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
|
||||||
|
agriculture_machinery_electric_efficiency: 0.3 # electricity per use
|
||||||
shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
|
shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
|
||||||
|
shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
|
||||||
|
shipping_hydrogen_share: 1 # 1 means all hydrogen FC
|
||||||
|
# 2020: 0
|
||||||
|
# 2025: 0
|
||||||
|
# 2030: 0.05
|
||||||
|
# 2035: 0.15
|
||||||
|
# 2040: 0.3
|
||||||
|
# 2045: 0.6
|
||||||
|
# 2050: 1
|
||||||
time_dep_hp_cop: true #time dependent heat pump coefficient of performance
|
time_dep_hp_cop: true #time dependent heat pump coefficient of performance
|
||||||
heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
|
heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
|
||||||
# conservatively high to cover hot water and space heating in poorly-insulated buildings
|
# conservatively high to cover hot water and space heating in poorly-insulated buildings
|
||||||
reduce_space_heat_exogenously: true # reduces space heat demand by a given factor (applied before losses in DH)
|
reduce_space_heat_exogenously: true # reduces space heat demand by a given factor (applied before losses in DH)
|
||||||
# this can represent e.g. building renovation, building demolition, or if
|
# this can represent e.g. building renovation, building demolition, or if
|
||||||
# the factor is negative: increasing floor area, increased thermal comfort, population growth
|
# the factor is negative: increasing floor area, increased thermal comfort, population growth
|
||||||
reduce_space_heat_exogenously_factor: # per unit reduction in space heat demand
|
reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
|
||||||
# the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
|
# the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
|
||||||
2020: 0.10 # this results in a space heat demand reduction of 10%
|
# 2020: 0.10 # this results in a space heat demand reduction of 10%
|
||||||
2025: 0.09 # first heat demand increases compared to 2020 because of larger floor area per capita
|
# 2025: 0.09 # first heat demand increases compared to 2020 because of larger floor area per capita
|
||||||
2030: 0.09
|
# 2030: 0.09
|
||||||
2035: 0.11
|
# 2035: 0.11
|
||||||
2040: 0.16
|
# 2040: 0.16
|
||||||
2045: 0.21
|
# 2045: 0.21
|
||||||
2050: 0.29
|
# 2050: 0.29
|
||||||
retrofitting : # co-optimises building renovation to reduce space heat demand
|
retrofitting : # co-optimises building renovation to reduce space heat demand
|
||||||
retro_endogen: false # co-optimise space heat savings
|
retro_endogen: false # co-optimise space heat savings
|
||||||
cost_factor: 1.0 # weight costs for building renovation
|
cost_factor: 1.0 # weight costs for building renovation
|
||||||
@ -212,7 +234,8 @@ sector:
|
|||||||
co2_vent: true
|
co2_vent: true
|
||||||
SMR: true
|
SMR: true
|
||||||
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
|
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
|
||||||
co2_sequestration_cost: 20 #EUR/tCO2 for transport and sequestration of CO2
|
co2_sequestration_cost: 10 #EUR/tCO2 for sequestration of CO2
|
||||||
|
co2_network: false
|
||||||
cc_fraction: 0.9 # default fraction of CO2 captured with post-combustion capture
|
cc_fraction: 0.9 # default fraction of CO2 captured with post-combustion capture
|
||||||
hydrogen_underground_storage: true
|
hydrogen_underground_storage: true
|
||||||
use_fischer_tropsch_waste_heat: true
|
use_fischer_tropsch_waste_heat: true
|
||||||
@ -228,25 +251,61 @@ sector:
|
|||||||
H2_retrofit_capacity_per_CH4: 0.6 # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
|
H2_retrofit_capacity_per_CH4: 0.6 # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
|
||||||
gas_distribution_grid: true
|
gas_distribution_grid: true
|
||||||
gas_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv
|
gas_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv
|
||||||
|
biomass_transport: false # biomass transport between nodes
|
||||||
conventional_generation: # generator : carrier
|
conventional_generation: # generator : carrier
|
||||||
OCGT: gas
|
OCGT: gas
|
||||||
|
|
||||||
|
|
||||||
industry:
|
industry:
|
||||||
St_primary_fraction: 0.3 # fraction of steel produced via primary route (DRI + EAF) versus secondary route (EAF); today fraction is 0.6
|
St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
|
||||||
|
# 2020: 0.6
|
||||||
|
# 2025: 0.55
|
||||||
|
# 2030: 0.5
|
||||||
|
# 2035: 0.45
|
||||||
|
# 2040: 0.4
|
||||||
|
# 2045: 0.35
|
||||||
|
# 2050: 0.3
|
||||||
|
DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
|
||||||
|
# 2020: 0
|
||||||
|
# 2025: 0
|
||||||
|
# 2030: 0.05
|
||||||
|
# 2035: 0.2
|
||||||
|
# 2040: 0.4
|
||||||
|
# 2045: 0.7
|
||||||
|
# 2050: 1
|
||||||
H2_DRI: 1.7 #H2 consumption in Direct Reduced Iron (DRI), MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
|
H2_DRI: 1.7 #H2 consumption in Direct Reduced Iron (DRI), MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
|
||||||
elec_DRI: 0.322 #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
|
elec_DRI: 0.322 #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
|
||||||
Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
|
Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
|
||||||
|
# 2020: 0.4
|
||||||
|
# 2025: 0.375
|
||||||
|
# 2030: 0.35
|
||||||
|
# 2035: 0.325
|
||||||
|
# 2040: 0.3
|
||||||
|
# 2045: 0.25
|
||||||
|
# 2050: 0.2
|
||||||
MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
|
MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
|
||||||
MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
|
MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
|
||||||
MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
|
MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
|
||||||
MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
|
MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
|
||||||
NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
|
NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
|
||||||
petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
|
petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
|
||||||
HVC_primary_fraction: 1.0 #fraction of current non-ammonia basic chemicals produced via primary route
|
HVC_primary_fraction: 1. # fraction of today's HVC produced via primary route
|
||||||
|
HVC_mechanical_recycling_fraction: 0. # fraction of today's HVC produced via mechanical recycling
|
||||||
|
HVC_chemical_recycling_fraction: 0. # fraction of today's HVC produced via chemical recycling
|
||||||
|
HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
|
||||||
|
MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
|
||||||
|
MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
|
||||||
|
chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
|
||||||
|
MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
|
||||||
|
MWh_H2_per_tCl: -0.9372 # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
|
||||||
|
methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
|
||||||
|
MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
|
||||||
|
MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
|
||||||
hotmaps_locate_missing: false
|
hotmaps_locate_missing: false
|
||||||
reference_year: 2015
|
reference_year: 2015
|
||||||
|
# references:
|
||||||
|
# DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
|
||||||
|
# Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
|
||||||
|
|
||||||
costs:
|
costs:
|
||||||
lifetime: 25 #default lifetime
|
lifetime: 25 #default lifetime
|
||||||
@ -360,6 +419,7 @@ plotting:
|
|||||||
- solar thermal collector
|
- solar thermal collector
|
||||||
- central solar thermal collector
|
- central solar thermal collector
|
||||||
tech_colors:
|
tech_colors:
|
||||||
|
# wind
|
||||||
onwind: "#235ebc"
|
onwind: "#235ebc"
|
||||||
onshore wind: "#235ebc"
|
onshore wind: "#235ebc"
|
||||||
offwind: "#6895dd"
|
offwind: "#6895dd"
|
||||||
@ -368,117 +428,161 @@ plotting:
|
|||||||
offshore wind (AC): "#6895dd"
|
offshore wind (AC): "#6895dd"
|
||||||
offwind-dc: "#74c6f2"
|
offwind-dc: "#74c6f2"
|
||||||
offshore wind (DC): "#74c6f2"
|
offshore wind (DC): "#74c6f2"
|
||||||
wave: '#004444'
|
# water
|
||||||
hydro: '#3B5323'
|
hydro: '#298c81'
|
||||||
hydro reservoir: '#3B5323'
|
hydro reservoir: '#298c81'
|
||||||
ror: '#78AB46'
|
ror: '#3dbfb0'
|
||||||
run of river: '#78AB46'
|
run of river: '#3dbfb0'
|
||||||
hydroelectricity: '#006400'
|
hydroelectricity: '#298c81'
|
||||||
|
PHS: '#51dbcc'
|
||||||
|
wave: '#a7d4cf'
|
||||||
|
# solar
|
||||||
solar: "#f9d002"
|
solar: "#f9d002"
|
||||||
solar PV: "#f9d002"
|
solar PV: "#f9d002"
|
||||||
solar thermal: coral
|
solar thermal: '#ffbf2b'
|
||||||
solar rooftop: '#ffef60'
|
solar rooftop: '#ffea80'
|
||||||
OCGT: wheat
|
# gas
|
||||||
OCGT marginal: sandybrown
|
OCGT: '#e0986c'
|
||||||
OCGT-heat: '#ee8340'
|
OCGT marginal: '#e0986c'
|
||||||
gas boiler: '#ee8340'
|
OCGT-heat: '#e0986c'
|
||||||
gas boilers: '#ee8340'
|
gas boiler: '#db6a25'
|
||||||
gas boiler marginal: '#ee8340'
|
gas boilers: '#db6a25'
|
||||||
gas-to-power/heat: '#ee8340'
|
gas boiler marginal: '#db6a25'
|
||||||
gas: brown
|
gas: '#e05b09'
|
||||||
Gas pipeline : brown
|
natural gas: '#e05b09'
|
||||||
natural gas: brown
|
CCGT: '#a85522'
|
||||||
SMR: '#4F4F2F'
|
CCGT marginal: '#a85522'
|
||||||
SMR CC: '#6f6f42'
|
gas for industry co2 to atmosphere: '#692e0a'
|
||||||
oil: '#B5A642'
|
gas for industry co2 to stored: '#8a3400'
|
||||||
oil boiler: '#B5A677'
|
gas for industry: '#853403'
|
||||||
lines: k
|
gas for industry CC: '#692e0a'
|
||||||
transmission lines: k
|
gas pipeline: '#ebbca0'
|
||||||
H2: m
|
Gas pipeline: '#ebbca0'
|
||||||
hydrogen storage: m
|
# oil
|
||||||
battery: slategray
|
oil: '#c9c9c9'
|
||||||
battery storage: slategray
|
oil boiler: '#adadad'
|
||||||
home battery: '#614700'
|
agriculture machinery oil: '#949494'
|
||||||
home battery storage: '#614700'
|
shipping oil: "#808080"
|
||||||
Nuclear: r
|
land transport oil: '#afafaf'
|
||||||
Nuclear marginal: r
|
# nuclear
|
||||||
nuclear: r
|
Nuclear: '#ff8c00'
|
||||||
uranium: r
|
Nuclear marginal: '#ff8c00'
|
||||||
Coal: k
|
nuclear: '#ff8c00'
|
||||||
coal: k
|
uranium: '#ff8c00'
|
||||||
Coal marginal: k
|
# coal
|
||||||
Lignite: grey
|
Coal: '#545454'
|
||||||
lignite: grey
|
coal: '#545454'
|
||||||
Lignite marginal: grey
|
Coal marginal: '#545454'
|
||||||
CCGT: '#ee8340'
|
solid: '#545454'
|
||||||
CCGT marginal: '#ee8340'
|
Lignite: '#826837'
|
||||||
heat pumps: '#76EE00'
|
lignite: '#826837'
|
||||||
heat pump: '#76EE00'
|
Lignite marginal: '#826837'
|
||||||
air heat pump: '#76EE00'
|
# biomass
|
||||||
ground heat pump: '#40AA00'
|
biogas: '#e3d37d'
|
||||||
power-to-heat: '#40AA00'
|
biomass: '#baa741'
|
||||||
resistive heater: pink
|
solid biomass: '#baa741'
|
||||||
Sabatier: '#FF1493'
|
solid biomass transport: '#baa741'
|
||||||
methanation: '#FF1493'
|
solid biomass for industry: '#7a6d26'
|
||||||
power-to-gas: '#FF1493'
|
solid biomass for industry CC: '#47411c'
|
||||||
power-to-liquid: '#FFAAE9'
|
solid biomass for industry co2 from atmosphere: '#736412'
|
||||||
helmeth: '#7D0552'
|
solid biomass for industry co2 to stored: '#47411c'
|
||||||
DAC: '#E74C3C'
|
# power transmission
|
||||||
co2 stored: '#123456'
|
lines: '#6c9459'
|
||||||
CO2 sequestration: '#123456'
|
transmission lines: '#6c9459'
|
||||||
CC: k
|
electricity distribution grid: '#97ad8c'
|
||||||
co2: '#123456'
|
# electricity demand
|
||||||
co2 vent: '#654321'
|
Electric load: '#110d63'
|
||||||
solid biomass for industry co2 from atmosphere: '#654321'
|
electric demand: '#110d63'
|
||||||
solid biomass for industry co2 to stored: '#654321'
|
electricity: '#110d63'
|
||||||
gas for industry co2 to atmosphere: '#654321'
|
industry electricity: '#2d2a66'
|
||||||
gas for industry co2 to stored: '#654321'
|
industry new electricity: '#2d2a66'
|
||||||
Fischer-Tropsch: '#44DD33'
|
agriculture electricity: '#494778'
|
||||||
kerosene for aviation: '#44BB11'
|
# battery + EVs
|
||||||
naphtha for industry: '#44FF55'
|
battery: '#ace37f'
|
||||||
land transport oil: '#44DD33'
|
battery storage: '#ace37f'
|
||||||
water tanks: '#BBBBBB'
|
home battery: '#80c944'
|
||||||
hot water storage: '#BBBBBB'
|
home battery storage: '#80c944'
|
||||||
hot water charging: '#BBBBBB'
|
BEV charger: '#baf238'
|
||||||
hot water discharging: '#999999'
|
V2G: '#e5ffa8'
|
||||||
CHP: r
|
land transport EV: '#baf238'
|
||||||
CHP heat: r
|
Li ion: '#baf238'
|
||||||
CHP electric: r
|
# hot water storage
|
||||||
PHS: g
|
water tanks: '#e69487'
|
||||||
Ambient: k
|
hot water storage: '#e69487'
|
||||||
Electric load: b
|
hot water charging: '#e69487'
|
||||||
Heat load: r
|
hot water discharging: '#e69487'
|
||||||
heat: darkred
|
# heat demand
|
||||||
rural heat: '#880000'
|
Heat load: '#cc1f1f'
|
||||||
central heat: '#b22222'
|
heat: '#cc1f1f'
|
||||||
decentral heat: '#800000'
|
heat demand: '#cc1f1f'
|
||||||
low-temperature heat for industry: '#991111'
|
rural heat: '#ff5c5c'
|
||||||
process heat: '#FF3333'
|
central heat: '#cc1f1f'
|
||||||
heat demand: darkred
|
decentral heat: '#750606'
|
||||||
electric demand: k
|
low-temperature heat for industry: '#8f2727'
|
||||||
Li ion: grey
|
process heat: '#ff0000'
|
||||||
district heating: '#CC4E5C'
|
agriculture heat: '#d9a5a5'
|
||||||
retrofitting: purple
|
# heat supply
|
||||||
building retrofitting: purple
|
heat pumps: '#2fb537'
|
||||||
BEV charger: grey
|
heat pump: '#2fb537'
|
||||||
V2G: grey
|
air heat pump: '#36eb41'
|
||||||
land transport EV: grey
|
ground heat pump: '#2fb537'
|
||||||
electricity: k
|
Ambient: '#98eb9d'
|
||||||
gas for industry: '#333333'
|
CHP: '#8a5751'
|
||||||
gas for industry CC: '#404040'
|
CHP CC: '#634643'
|
||||||
solid biomass for industry: '#555555'
|
CHP heat: '#8a5751'
|
||||||
solid biomass for industry CC: '#555555'
|
CHP electric: '#8a5751'
|
||||||
industry electricity: '#222222'
|
district heating: '#e8beac'
|
||||||
industry new electricity: '#222222'
|
resistive heater: '#d8f9b8'
|
||||||
|
retrofitting: '#8487e8'
|
||||||
|
building retrofitting: '#8487e8'
|
||||||
|
# hydrogen
|
||||||
|
H2 for industry: "#f073da"
|
||||||
|
H2 for shipping: "#ebaee0"
|
||||||
|
H2: '#bf13a0'
|
||||||
|
hydrogen: '#bf13a0'
|
||||||
|
SMR: '#870c71'
|
||||||
|
SMR CC: '#4f1745'
|
||||||
|
H2 liquefaction: '#d647bd'
|
||||||
|
hydrogen storage: '#bf13a0'
|
||||||
|
H2 storage: '#bf13a0'
|
||||||
|
land transport fuel cell: '#6b3161'
|
||||||
|
H2 pipeline: '#f081dc'
|
||||||
|
H2 Fuel Cell: '#c251ae'
|
||||||
|
H2 Electrolysis: '#ff29d9'
|
||||||
|
# syngas
|
||||||
|
Sabatier: '#9850ad'
|
||||||
|
methanation: '#c44ce6'
|
||||||
|
methane: '#c44ce6'
|
||||||
|
helmeth: '#e899ff'
|
||||||
|
# synfuels
|
||||||
|
Fischer-Tropsch: '#25c49a'
|
||||||
|
liquid: '#25c49a'
|
||||||
|
kerosene for aviation: '#a1ffe6'
|
||||||
|
naphtha for industry: '#57ebc4'
|
||||||
|
# co2
|
||||||
|
CC: '#f29dae'
|
||||||
|
CCS: '#f29dae'
|
||||||
|
CO2 sequestration: '#f29dae'
|
||||||
|
DAC: '#ff5270'
|
||||||
|
co2 stored: '#f2385a'
|
||||||
|
co2: '#f29dae'
|
||||||
|
co2 vent: '#ffd4dc'
|
||||||
|
CO2 pipeline: '#f5627f'
|
||||||
|
# emissions
|
||||||
|
process emissions CC: '#000000'
|
||||||
|
process emissions: '#222222'
|
||||||
process emissions to stored: '#444444'
|
process emissions to stored: '#444444'
|
||||||
process emissions to atmosphere: '#888888'
|
process emissions to atmosphere: '#888888'
|
||||||
process emissions: '#222222'
|
oil emissions: '#aaaaaa'
|
||||||
process emissions CC: '#484848'
|
shipping oil emissions: "#555555"
|
||||||
oil emissions: '#666666'
|
land transport oil emissions: '#777777'
|
||||||
land transport oil emissions: '#666666'
|
agriculture machinery oil emissions: '#333333'
|
||||||
land transport fuel cell: '#AAAAAA'
|
# other
|
||||||
biogas: '#800000'
|
shipping: '#03a2ff'
|
||||||
solid biomass: '#DAA520'
|
power-to-heat: '#2fb537'
|
||||||
today: '#D2691E'
|
power-to-gas: '#c44ce6'
|
||||||
shipping: '#6495ED'
|
power-to-H2: '#ff29d9'
|
||||||
electricity distribution grid: '#333333'
|
power-to-liquid: '#25c49a'
|
||||||
|
gas-to-power/heat: '#ee8340'
|
||||||
|
waste: '#e3d37d'
|
||||||
|
other: '#000000'
|
||||||
|
|||||||
34
data/district_heat_share.csv
Normal file
34
data/district_heat_share.csv
Normal file
@ -0,0 +1,34 @@
|
|||||||
|
country,share to satisfy heat demand (residential) in percent,capacity[MWth]
|
||||||
|
AT,14,11200
|
||||||
|
BG,16,6162
|
||||||
|
BA,8,
|
||||||
|
HR,6.3,2221
|
||||||
|
CZ,40,
|
||||||
|
DK,65,
|
||||||
|
FI,38,23390
|
||||||
|
FR,5,
|
||||||
|
DE,13.8,
|
||||||
|
HU,7.92875588637399,8549
|
||||||
|
IS,90,8079000
|
||||||
|
IE,0.8,
|
||||||
|
IT,3,8727
|
||||||
|
LV,73,2254
|
||||||
|
LT,56,
|
||||||
|
MK,23.7745607009008,636
|
||||||
|
NO,4,3400
|
||||||
|
PL,42,54912
|
||||||
|
PT,0.070754716981132,34
|
||||||
|
RS,25,5821
|
||||||
|
SI,8.86,1739
|
||||||
|
ES,0.251589260787732,1273
|
||||||
|
SE,50.4,
|
||||||
|
UK,2,
|
||||||
|
BY,70,
|
||||||
|
EE,52,5406
|
||||||
|
KO,3,207
|
||||||
|
RO,23,9962
|
||||||
|
SK,54,15000
|
||||||
|
NL,4,9800
|
||||||
|
CH,4,2792
|
||||||
|
AL,0,
|
||||||
|
ME,0,
|
||||||
|
25
data/heat_load_profile_DK_AdamJensen.csv
Normal file
25
data/heat_load_profile_DK_AdamJensen.csv
Normal file
@ -0,0 +1,25 @@
|
|||||||
|
hour,weekday,weekend
|
||||||
|
0,0.9181438689,0.9421512708
|
||||||
|
1,0.9172359071,0.9400891069
|
||||||
|
2,0.9269464481,0.9461062015
|
||||||
|
3,0.9415047932,0.9535084941
|
||||||
|
4,0.9656299507,0.9651094993
|
||||||
|
5,1.0221166443,0.9834676747
|
||||||
|
6,1.1553090493,1.0124171051
|
||||||
|
7,1.2093411031,1.0446615927
|
||||||
|
8,1.1470295942,1.088203419
|
||||||
|
9,1.0877191341,1.1110334576
|
||||||
|
10,1.0418327372,1.0926752822
|
||||||
|
11,1.0062977133,1.055488209
|
||||||
|
12,0.9837030359,1.0251266112
|
||||||
|
13,0.9667570278,0.9990015154
|
||||||
|
14,0.9548320932,0.9782897278
|
||||||
|
15,0.9509232061,0.9698167237
|
||||||
|
16,0.9636973319,0.974288587
|
||||||
|
17,0.9799372563,0.9886456216
|
||||||
|
18,1.0046501848,1.0084159643
|
||||||
|
19,1.0079452419,1.0171243296
|
||||||
|
20,0.9860566481,0.9994722379
|
||||||
|
21,0.9705228074,0.982761591
|
||||||
|
22,0.9586485819,0.9698167237
|
||||||
|
23,0.9335023778,0.9515079292
|
||||||
|
30
data/urban_percent.csv
Normal file
30
data/urban_percent.csv
Normal file
@ -0,0 +1,30 @@
|
|||||||
|
AT,66
|
||||||
|
BA,40
|
||||||
|
BE,98
|
||||||
|
BG,74
|
||||||
|
CH,74
|
||||||
|
CZ,73
|
||||||
|
DE,75
|
||||||
|
DK,88
|
||||||
|
EE,68
|
||||||
|
ES,80
|
||||||
|
FI,84
|
||||||
|
FR,80
|
||||||
|
GB,83
|
||||||
|
GR,78
|
||||||
|
HR,59
|
||||||
|
HU,71
|
||||||
|
IE,63
|
||||||
|
IT,69
|
||||||
|
LT,67
|
||||||
|
LU,90
|
||||||
|
LV,67
|
||||||
|
NL,90
|
||||||
|
NO,80
|
||||||
|
PL,61
|
||||||
|
PT,63
|
||||||
|
RO,55
|
||||||
|
RS,56
|
||||||
|
SE,86
|
||||||
|
SI,50
|
||||||
|
SK,54
|
||||||
|
@ -62,17 +62,17 @@ master_doc = 'index'
|
|||||||
|
|
||||||
# General information about the project.
|
# General information about the project.
|
||||||
project = u'PyPSA-Eur-Sec'
|
project = u'PyPSA-Eur-Sec'
|
||||||
copyright = u'2019-2020 Tom Brown (KIT), Marta Victoria (Aarhus University), Lisa Zeyen (KIT)'
|
copyright = u'2019-2021 Tom Brown (KIT, TUB), Marta Victoria (Aarhus University), Lisa Zeyen (KIT, TUB), Fabian Neumann (TUB)'
|
||||||
author = u'2019-2020 Tom Brown (KIT), Marta Victoria (Aarhus University), Lisa Zeyen (KIT)'
|
author = u'2019-2021 Tom Brown (KIT, TUB), Marta Victoria (Aarhus University), Lisa Zeyen (KIT, TUB), Fabian Neumann (TUB)'
|
||||||
|
|
||||||
# The version info for the project you're documenting, acts as replacement for
|
# The version info for the project you're documenting, acts as replacement for
|
||||||
# |version| and |release|, also used in various other places throughout the
|
# |version| and |release|, also used in various other places throughout the
|
||||||
# built documents.
|
# built documents.
|
||||||
#
|
#
|
||||||
# The short X.Y version.
|
# The short X.Y version.
|
||||||
version = u'0.5'
|
version = u'0.6'
|
||||||
# The full version, including alpha/beta/rc tags.
|
# The full version, including alpha/beta/rc tags.
|
||||||
release = u'0.5.0'
|
release = u'0.6.0'
|
||||||
|
|
||||||
# The language for content autogenerated by Sphinx. Refer to documentation
|
# The language for content autogenerated by Sphinx. Refer to documentation
|
||||||
# for a list of supported languages.
|
# for a list of supported languages.
|
||||||
|
|||||||
@ -2,11 +2,11 @@ description,file/folder,licence,source
|
|||||||
JRC IDEES database,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees
|
JRC IDEES database,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees
|
||||||
urban/rural fraction,urban_percent.csv,unknown,unknown
|
urban/rural fraction,urban_percent.csv,unknown,unknown
|
||||||
JRC biomass potentials,biomass/,unknown,https://doi.org/10.2790/39014
|
JRC biomass potentials,biomass/,unknown,https://doi.org/10.2790/39014
|
||||||
|
JRC ENSPRESO biomass potentials,remote,CC BY 4.0,https://data.jrc.ec.europa.eu/dataset/74ed5a04-7d74-4807-9eab-b94774309d9f
|
||||||
EEA emission statistics,eea/UNFCCC_v23.csv,EEA standard re-use policy,https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
|
EEA emission statistics,eea/UNFCCC_v23.csv,EEA standard re-use policy,https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
|
||||||
Eurostat Energy Balances,eurostat-energy_balances-*/,Eurostat,https://ec.europa.eu/eurostat/web/energy/data/energy-balances
|
Eurostat Energy Balances,eurostat-energy_balances-*/,Eurostat,https://ec.europa.eu/eurostat/web/energy/data/energy-balances
|
||||||
Swiss energy statistics from Swiss Federal Office of Energy,switzerland-sfoe/,unknown,http://www.bfe.admin.ch/themen/00526/00541/00542/02167/index.html?dossier_id=02169
|
Swiss energy statistics from Swiss Federal Office of Energy,switzerland-sfoe/,unknown,http://www.bfe.admin.ch/themen/00526/00541/00542/02167/index.html?dossier_id=02169
|
||||||
BASt emobility statistics,emobility/,unknown,http://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/Stundenwerte.html?nn=626916
|
BASt emobility statistics,emobility/,unknown,http://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/Stundenwerte.html?nn=626916
|
||||||
timezone mappings,timezone_mappings.csv,CC BY 4.0,Tom Brown
|
|
||||||
BDEW heating profile,heat_load_profile_BDEW.csv,unknown,https://github.com/oemof/demandlib
|
BDEW heating profile,heat_load_profile_BDEW.csv,unknown,https://github.com/oemof/demandlib
|
||||||
heating profiles for Aarhus,heat_load_profile_DK_AdamJensen.csv,unknown,Adam Jensen MA thesis at Aarhus University
|
heating profiles for Aarhus,heat_load_profile_DK_AdamJensen.csv,unknown,Adam Jensen MA thesis at Aarhus University
|
||||||
George Lavidas wind/wave costs,WindWaveWEC_GLTB.xlsx,unknown,George Lavidas
|
George Lavidas wind/wave costs,WindWaveWEC_GLTB.xlsx,unknown,George Lavidas
|
||||||
@ -24,3 +24,6 @@ Comparative level investment,comparative_level_investment.csv,Eurostat,https://e
|
|||||||
Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en
|
Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en
|
||||||
Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
|
Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
|
||||||
Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unkown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
|
Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unkown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
|
||||||
|
District heating most countries,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees,,
|
||||||
|
District heating missing countries,district_heat_share.csv,unkown,https://www.euroheat.org/knowledge-hub/country-profiles,,
|
||||||
|
|
||||||
|
|||||||
|
Can't render this file because it has a wrong number of fields in line 27.
|
@ -29,6 +29,11 @@ heating, biomass, industry and industrial feedstocks. This completes
|
|||||||
the energy system and includes all greenhouse gas emitters except
|
the energy system and includes all greenhouse gas emitters except
|
||||||
waste management, agriculture, forestry and land use.
|
waste management, agriculture, forestry and land use.
|
||||||
|
|
||||||
|
.. note::
|
||||||
|
More about the current model capabilities and preliminary results
|
||||||
|
can be found in `a recent presentation at EMP-E <https://nworbmot.org/energy/brown-empe.pdf>`_
|
||||||
|
and the the following `preprint with a description of the industry sector <https://arxiv.org/abs/2109.09563>`_.
|
||||||
|
|
||||||
This diagram gives an overview of the sectors and the links between
|
This diagram gives an overview of the sectors and the links between
|
||||||
them:
|
them:
|
||||||
|
|
||||||
@ -61,9 +66,25 @@ PyPSA-Eur-Sec is the different extra_functionality required to build
|
|||||||
storage and CHP constraints.
|
storage and CHP constraints.
|
||||||
|
|
||||||
|
|
||||||
PyPSA-Eur-Sec is designed to be imported into the open toolbox `PyPSA <https://www.pypsa.org>`_ for which `documentation <https://pypsa.org/doc>`_ is available as well.
|
PyPSA-Eur-Sec is designed to be imported into the open toolbox `PyPSA
|
||||||
|
<https://www.pypsa.org>`_ for which `documentation <https://pypsa.org/doc>`_ is
|
||||||
|
available as well.
|
||||||
|
|
||||||
This project is maintained by the `Energy System Modelling group <https://www.iai.kit.edu/english/2338.php>`_ at the `Institute for Automation and Applied Informatics <https://www.iai.kit.edu/english/index.php>`_ at the `Karlsruhe Institute of Technology <http://www.kit.edu/english/index.php>`_. The group is funded by the `Helmholtz Association <https://www.helmholtz.de/en/>`_ until 2024. Previous versions were developed by the `Renewable Energy Group <https://fias.uni-frankfurt.de/physics/schramm/renewable-energy-system-and-network-analysis/>`_ at `FIAS <https://fias.uni-frankfurt.de/>`_ to carry out simulations for the `CoNDyNet project <http://condynet.de/>`_, financed by the `German Federal Ministry for Education and Research (BMBF) <https://www.bmbf.de/en/index.html>`_ as part of the `Stromnetze Research Initiative <http://forschung-stromnetze.info/projekte/grundlagen-und-konzepte-fuer-effiziente-dezentrale-stromnetze/>`_.
|
This project is currently maintained by the `Department of Digital
|
||||||
|
Transformation in Energy Systems <https://tub-ensys.github.io>`_ at the
|
||||||
|
`Technical University of Berlin <https://www.tu.berlin>`_. Previous versions
|
||||||
|
were developed by the `Energy System Modelling group
|
||||||
|
<https://www.iai.kit.edu/english/2338.php>`_ at the `Institute for Automation
|
||||||
|
and Applied Informatics <https://www.iai.kit.edu/english/index.php>`_ at the
|
||||||
|
`Karlsruhe Institute of Technology <http://www.kit.edu/english/index.php>`_
|
||||||
|
which was funded by the `Helmholtz Association <https://www.helmholtz.de/en/>`_,
|
||||||
|
and by the `Renewable Energy Group
|
||||||
|
<https://fias.uni-frankfurt.de/physics/schramm/renewable-energy-system-and-network-analysis/>`_
|
||||||
|
at `FIAS <https://fias.uni-frankfurt.de/>`_ to carry out simulations for the
|
||||||
|
`CoNDyNet project <http://condynet.de/>`_, financed by the `German Federal
|
||||||
|
Ministry for Education and Research (BMBF) <https://www.bmbf.de/en/index.html>`_
|
||||||
|
as part of the `Stromnetze Research Initiative
|
||||||
|
<http://forschung-stromnetze.info/projekte/grundlagen-und-konzepte-fuer-effiziente-dezentrale-stromnetze/>`_.
|
||||||
|
|
||||||
|
|
||||||
Documentation
|
Documentation
|
||||||
@ -134,7 +155,7 @@ it.
|
|||||||
Licence
|
Licence
|
||||||
=======
|
=======
|
||||||
|
|
||||||
The code in PyPSA-Eur-Sec is released as free software under the `GPLv3
|
The code in PyPSA-Eur-Sec is released as free software under the
|
||||||
<http://www.gnu.org/licenses/gpl-3.0.en.html>`_, see
|
`MIT license <https://opensource.org/licenses/MIT>`_, see
|
||||||
`LICENSE <https://github.com/PyPSA/pypsa-eur-sec/blob/master/LICENSE.txt>`_.
|
`LICENSE <https://github.com/PyPSA/pypsa-eur-sec/blob/master/LICENSE.txt>`_.
|
||||||
However, different licenses and terms of use may apply to the various input data.
|
However, different licenses and terms of use may apply to the various input data.
|
||||||
|
|||||||
@ -66,15 +66,15 @@ Data requirements
|
|||||||
=================
|
=================
|
||||||
|
|
||||||
Small data files are included directly in the git repository, while
|
Small data files are included directly in the git repository, while
|
||||||
larger ones are archived in a data bundle. The data bundle's size is
|
larger ones are archived in a data bundle on zenodo (`10.5281/zenodo.5546517 <https://doi.org/10.5281/zenodo.5546517>`_).
|
||||||
around 640 MB.
|
The data bundle's size is around 640 MB.
|
||||||
|
|
||||||
To download and extract the data bundle on the command line:
|
To download and extract the data bundle on the command line:
|
||||||
|
|
||||||
.. code:: bash
|
.. code:: bash
|
||||||
|
`
|
||||||
projects/pypsa-eur-sec/data % wget "https://nworbmot.org/pypsa-eur-sec-data-bundle-210418.tar.gz"
|
projects/pypsa-eur-sec/data % wget "https://zenodo.org/record/5546517/files/pypsa-eur-sec-data-bundle.tar.gz"
|
||||||
projects/pypsa-eur-sec/data % tar xvzf pypsa-eur-sec-data-bundle-210418.tar.gz
|
projects/pypsa-eur-sec/data % tar xvzf pypsa-eur-sec-data-bundle.tar.gz
|
||||||
|
|
||||||
|
|
||||||
The data licences and sources are given in the following table.
|
The data licences and sources are given in the following table.
|
||||||
@ -89,10 +89,8 @@ The data licences and sources are given in the following table.
|
|||||||
Set up the default configuration
|
Set up the default configuration
|
||||||
================================
|
================================
|
||||||
|
|
||||||
First make your own copy of the ``config.yaml``. For overnight
|
First make your own copy of the ``config.yaml`` based on
|
||||||
scenarios, use ``config.default.yaml``. For a pathway optimization
|
``config.default.yaml``. For example:
|
||||||
with myopic foresight (which is still experimental), use
|
|
||||||
``config.myopic.yaml``. For example:
|
|
||||||
|
|
||||||
.. code:: bash
|
.. code:: bash
|
||||||
|
|
||||||
|
|||||||
@ -6,61 +6,192 @@ Future release
|
|||||||
==============
|
==============
|
||||||
|
|
||||||
.. note::
|
.. note::
|
||||||
This unreleased version currently requires the master branches of PyPSA, PyPSA-Eur, and the technology-data repository.
|
This unreleased version currently may require the master branches of PyPSA, PyPSA-Eur, and the technology-data repository.
|
||||||
|
|
||||||
|
PyPSA-Eur-Sec 0.6.0 (4 October 2021)
|
||||||
|
====================================
|
||||||
|
|
||||||
|
This release includes
|
||||||
|
improvements regarding the basic chemical production,
|
||||||
|
the addition of plastics recycling,
|
||||||
|
the addition of the agriculture, forestry and fishing sector,
|
||||||
|
more regionally resolved biomass potentials,
|
||||||
|
CO2 pipeline transport and storage, and
|
||||||
|
more options in setting exogenous transition paths,
|
||||||
|
besides many performance improvements.
|
||||||
|
|
||||||
|
This release is known to work with `PyPSA-Eur
|
||||||
|
<https://github.com/PyPSA/pypsa-eur>`_ Version 0.4.0, `Technology Data
|
||||||
|
<https://github.com/PyPSA/technology-data>`_ Version 0.3.0 and
|
||||||
|
`PyPSA <https://github.com/PyPSA/PyPSA>`_ Version 0.18.0.
|
||||||
|
|
||||||
|
Please note that the data bundle has also been updated.
|
||||||
|
|
||||||
|
|
||||||
|
**General**
|
||||||
|
|
||||||
|
* With this release, we change the license from copyleft GPLv3 to the more
|
||||||
|
liberal MIT license with the consent of all contributors.
|
||||||
|
|
||||||
|
|
||||||
|
**New features and functionality**
|
||||||
|
|
||||||
|
* Distinguish costs for home battery storage and inverter from utility-scale
|
||||||
|
battery costs.
|
||||||
|
|
||||||
|
* Separate basic chemicals into HVC (high-value chemicals), chlorine, methanol and ammonia
|
||||||
|
[`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].
|
||||||
|
|
||||||
|
* Add option to specify reuse, primary production, and mechanical and chemical
|
||||||
|
recycling fraction of platics
|
||||||
|
[`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].
|
||||||
|
|
||||||
|
* Include energy demands and CO2 emissions for the agriculture, forestry and fishing sector.
|
||||||
|
It is included by default through the option ``A`` in the ``sector_opts`` wildcard.
|
||||||
|
Part of the emissions (1.A.4.c) was previously assigned to "industry non-elec" in the ``co2_totals.csv``.
|
||||||
|
Hence, excluding the agriculture sector will now lead to a tighter CO2 limit.
|
||||||
|
Energy demands are taken from the JRC IDEES database (missing countries filled with eurostat data)
|
||||||
|
and are split into
|
||||||
|
electricity (lighting, ventilation, specific electricity uses, pumping devices (electric)),
|
||||||
|
heat (specific heat uses, low enthalpy heat)
|
||||||
|
machinery oil (motor drives, farming machine drives, pumping devices (diesel)).
|
||||||
|
Heat demand is assigned at "services rural heat" buses.
|
||||||
|
Electricity demands are added to low-voltage buses.
|
||||||
|
Time series for demands are constant and distributed inside countries by population
|
||||||
|
[`#147 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/147>`_].
|
||||||
|
|
||||||
|
* Include today's district heating shares in myopic optimisation and add option
|
||||||
|
to specify exogenous path for district heating share increase under ``sector:
|
||||||
|
district_heating:`` [`#149 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/149>`_].
|
||||||
|
|
||||||
|
* Added option for hydrogen liquefaction costs for hydrogen demand in shipping.
|
||||||
|
This introduces a new ``H2 liquid`` bus at each location. It is activated via
|
||||||
|
``sector: shipping_hydrogen_liquefaction: true``.
|
||||||
|
|
||||||
|
* The share of shipping transformed into hydrogen fuel cell can be now defined
|
||||||
|
for different years in the ``config.yaml`` file. The carbon emission from the
|
||||||
|
remaining share is treated as a negative load on the atmospheric carbon dioxide
|
||||||
|
bus, just like aviation and land transport emissions.
|
||||||
|
|
||||||
|
* The transformation of the Steel and Aluminium production can be now defined
|
||||||
|
for different years in the ``config.yaml`` file.
|
||||||
|
|
||||||
|
* Include the option to alter the maximum energy capacity of a store via the
|
||||||
|
``carrier+factor`` in the ``{sector_opts}`` wildcard. This can be useful for
|
||||||
|
sensitivity analyses. Example: ``co2 stored+e2`` multiplies the ``e_nom_max`` by
|
||||||
|
factor 2. In this example, ``e_nom_max`` represents the CO2 sequestration
|
||||||
|
potential in Europe.
|
||||||
|
|
||||||
|
* Use `JRC ENSPRESO database <https://data.jrc.ec.europa.eu/dataset/74ed5a04-7d74-4807-9eab-b94774309d9f>`_ to
|
||||||
|
spatially disaggregate biomass potentials to PyPSA-Eur regions based on
|
||||||
|
overlaps with NUTS2 regions from ENSPRESO (proportional to area) (`#151
|
||||||
|
<https://github.com/PyPSA/pypsa-eur-sec/pull/151>`_).
|
||||||
|
|
||||||
|
* Add option to regionally disaggregate biomass potential to individual nodes
|
||||||
|
(previously given per country, then distributed by population density within)
|
||||||
|
and allow the transport of solid biomass. The transport costs are determined
|
||||||
|
based on the `JRC-EU-Times Bioenergy report
|
||||||
|
<http://dx.doi.org/10.2790/01017>`_ in the new optional rule
|
||||||
|
``build_biomass_transport_costs``. Biomass transport can be activated with the
|
||||||
|
setting ``sector: biomass_transport: true``.
|
||||||
|
|
||||||
|
* Add option to regionally resolve CO2 storage and add CO2 pipeline transport
|
||||||
|
because geological storage potential,
|
||||||
|
CO2 utilisation sites and CO2 capture sites may be separated. The CO2 network
|
||||||
|
is built from zero based on the topology of the electricity grid (greenfield).
|
||||||
|
Pipelines are assumed to be bidirectional and lossless. Furthermore, neither
|
||||||
|
retrofitting of natural gas pipelines (required pressures are too high, 80-160
|
||||||
|
bar vs <80 bar) nor other modes of CO2 transport (by ship, road or rail) are
|
||||||
|
considered. The regional representation of CO2 is activated with the config
|
||||||
|
setting ``sector: co2_network: true`` but is deactivated by default. The
|
||||||
|
global limit for CO2 sequestration now applies to the sum of all CO2 stores
|
||||||
|
via an ``extra_functionality`` constraint.
|
||||||
|
|
||||||
|
* The myopic option can now be used together with different clustering for the
|
||||||
|
generators and the network. The existing renewable capacities are split evenly
|
||||||
|
among the regions in every country [`#144 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/144>`_].
|
||||||
|
|
||||||
|
* Add optional function to use ``geopy`` to locate entries of the Hotmaps
|
||||||
|
database of industrial sites with missing location based on city and country,
|
||||||
|
which reduces missing entries by half. It can be activated by setting
|
||||||
|
``industry: hotmaps_locate_missing: true``, takes a few minutes longer, and
|
||||||
|
should only be used if spatial resolution is coarser than city level.
|
||||||
|
|
||||||
|
|
||||||
|
**Performance and Structure**
|
||||||
|
|
||||||
* Extended use of ``multiprocessing`` for much better performance
|
* Extended use of ``multiprocessing`` for much better performance
|
||||||
(from up to 20 minutes to less than one minute).
|
(from up to 20 minutes to less than one minute).
|
||||||
* Compatibility with ``atlite>=0.2``. Older versions of ``atlite`` will no longer work.
|
|
||||||
* Handle most input files (or base directories) via ``snakemake.input``.
|
* Handle most input files (or base directories) via ``snakemake.input``.
|
||||||
|
|
||||||
* Use of ``mock_snakemake`` from PyPSA-Eur.
|
* Use of ``mock_snakemake`` from PyPSA-Eur.
|
||||||
* Update ``solve_network`` rule to match implementation in PyPSA-Eur by using ``n.ilopf()`` and remove outdated code using ``pyomo``.
|
|
||||||
Allows the new setting to skip iterated impedance updates with ``solving: options: skip_iterations: true``.
|
* Update ``solve_network`` rule to match implementation in PyPSA-Eur by using
|
||||||
|
``n.ilopf()`` and remove outdated code using ``pyomo``.
|
||||||
|
Allows the new setting to skip iterated impedance updates with ``solving:
|
||||||
|
options: skip_iterations: true``.
|
||||||
|
|
||||||
* The component attributes that are to be overridden are now stored in the folder
|
* The component attributes that are to be overridden are now stored in the folder
|
||||||
``data/override_component_attrs`` analogous to ``pypsa/component_attrs``.
|
``data/override_component_attrs`` analogous to ``pypsa/component_attrs``.
|
||||||
This reduces verbosity and also allows circumventing the ``n.madd()`` hack
|
This reduces verbosity and also allows circumventing the ``n.madd()`` hack
|
||||||
for individual components with non-default attributes.
|
for individual components with non-default attributes.
|
||||||
This data is also tracked in the Snakefile.
|
This data is also tracked in the Snakefile.
|
||||||
|
|
||||||
A function ``helper.override_component_attrs`` was added that loads this data
|
A function ``helper.override_component_attrs`` was added that loads this data
|
||||||
and can pass the overridden component attributes into ``pypsa.Network()``:
|
and can pass the overridden component attributes into ``pypsa.Network()``.
|
||||||
|
|
||||||
>>> from helper import override_component_attrs
|
|
||||||
>>> overrides = override_component_attrs(snakemake.input.overrides)
|
|
||||||
>>> n = pypsa.Network("mynetwork.nc", override_component_attrs=overrides)
|
|
||||||
|
|
||||||
* Add various parameters to ``config.default.yaml`` which were previously hardcoded inside the scripts
|
* Add various parameters to ``config.default.yaml`` which were previously hardcoded inside the scripts
|
||||||
(e.g. energy reference years, BEV settings, solar thermal collector models, geomap colours).
|
(e.g. energy reference years, BEV settings, solar thermal collector models, geomap colours).
|
||||||
|
|
||||||
* Removed stale industry demand rules ``build_industrial_energy_demand_per_country``
|
* Removed stale industry demand rules ``build_industrial_energy_demand_per_country``
|
||||||
and ``build_industrial_demand``. These are superseded with more regionally resolved rules.
|
and ``build_industrial_demand``. These are superseded with more regionally resolved rules.
|
||||||
|
|
||||||
* Use simpler and shorter ``gdf.sjoin()`` function to allocate industrial sites
|
* Use simpler and shorter ``gdf.sjoin()`` function to allocate industrial sites
|
||||||
from the Hotmaps database to onshore regions.
|
from the Hotmaps database to onshore regions.
|
||||||
|
|
||||||
This change also fixes a bug:
|
This change also fixes a bug:
|
||||||
The previous version allocated sites to the closest bus,
|
The previous version allocated sites to the closest bus,
|
||||||
but at country borders (where Voronoi cells are distorted by the borders),
|
but at country borders (where Voronoi cells are distorted by the borders),
|
||||||
this had resulted in e.g. a Spanish site close to the French border
|
this had resulted in e.g. a Spanish site close to the French border
|
||||||
being wrongly allocated to the French bus if the bus center was closer.
|
being wrongly allocated to the French bus if the bus center was closer.
|
||||||
* Bugfix: Corrected calculation of "gas for industry" carbon capture efficiency.
|
|
||||||
* Retrofitting rule is now only triggered if endogeneously optimised.
|
* Retrofitting rule is now only triggered if endogeneously optimised.
|
||||||
|
|
||||||
* Show progress in build rules with ``tqdm`` progress bars.
|
* Show progress in build rules with ``tqdm`` progress bars.
|
||||||
|
|
||||||
* Reduced verbosity of ``Snakefile`` through directory prefixes.
|
* Reduced verbosity of ``Snakefile`` through directory prefixes.
|
||||||
|
|
||||||
* Improve legibility of ``config.default.yaml`` and remove unused options.
|
* Improve legibility of ``config.default.yaml`` and remove unused options.
|
||||||
* Add optional function to use ``geopy`` to locate entries of the Hotmaps database of industrial sites
|
|
||||||
with missing location based on city and country, which reduces missing entries by half. It can be
|
|
||||||
activated by setting ``industry: hotmaps_locate_missing: true``, takes a few minutes longer,
|
|
||||||
and should only be used if spatial resolution is coarser than city level.
|
|
||||||
* Use the country-specific time zone mappings from ``pytz`` rather than a manual mapping.
|
* Use the country-specific time zone mappings from ``pytz`` rather than a manual mapping.
|
||||||
|
|
||||||
* A function ``add_carrier_buses()`` was added to the ``prepare_network`` rule to reduce code duplication.
|
* A function ``add_carrier_buses()`` was added to the ``prepare_network`` rule to reduce code duplication.
|
||||||
|
|
||||||
* In the ``prepare_network`` rule the cost and potential adjustment was moved into an
|
* In the ``prepare_network`` rule the cost and potential adjustment was moved into an
|
||||||
own function ``maybe_adjust_costs_and_potentials()``.
|
own function ``maybe_adjust_costs_and_potentials()``.
|
||||||
* Use ``matplotlibrc`` to set the default plotting style and backend``.
|
|
||||||
* Added benchmark files for each rule.
|
|
||||||
* Implements changes to ``n.snapshot_weightings`` in upcoming PyPSA version (cf. `PyPSA/#227 <https://github.com/PyPSA/PyPSA/pull/227>`_).
|
|
||||||
* New dependencies: ``tqdm``, ``atlite>=0.2.4``, ``pytz`` and ``geopy`` (optional).
|
|
||||||
These are included in the environment specifications of PyPSA-Eur.
|
|
||||||
* Consistent use of ``__main__`` block and further unspecific code cleaning.
|
|
||||||
* Distinguish costs for home battery storage and inverter from utility-scale battery costs.
|
|
||||||
|
|
||||||
|
* Use ``matplotlibrc`` to set the default plotting style and backend.
|
||||||
|
|
||||||
|
* Added benchmark files for each rule.
|
||||||
|
|
||||||
|
* Consistent use of ``__main__`` block and further unspecific code cleaning.
|
||||||
|
|
||||||
|
* Updated data bundle and moved data bundle to zenodo.org (`10.5281/zenodo.5546517 <https://doi.org/10.5281/zenodo.5546517>`_).
|
||||||
|
|
||||||
|
|
||||||
|
**Bugfixes and Compatibility**
|
||||||
|
|
||||||
|
* Compatibility with ``atlite>=0.2``. Older versions of ``atlite`` will no longer work.
|
||||||
|
|
||||||
|
* Corrected calculation of "gas for industry" carbon capture efficiency.
|
||||||
|
|
||||||
|
* Implemented changes to ``n.snapshot_weightings`` in PyPSA v0.18.0.
|
||||||
|
|
||||||
|
* Compatibility with ``xarray`` version 0.19.
|
||||||
|
|
||||||
|
* New dependencies: ``tqdm``, ``atlite>=0.2.4``, ``pytz`` and ``geopy`` (optional).
|
||||||
|
These are included in the environment specifications of PyPSA-Eur v0.4.0.
|
||||||
|
|
||||||
|
Many thanks to all who contributed to this release!
|
||||||
|
|
||||||
|
|
||||||
PyPSA-Eur-Sec 0.5.0 (21st May 2021)
|
PyPSA-Eur-Sec 0.5.0 (21st May 2021)
|
||||||
@ -242,4 +373,4 @@ To make a new release of the data bundle, make an archive of the files in ``data
|
|||||||
|
|
||||||
.. code:: bash
|
.. code:: bash
|
||||||
|
|
||||||
data % tar pczf pypsa-eur-sec-data-bundle-YYMMDD.tar.gz eea/UNFCCC_v23.csv switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility urban_percent.csv timezone_mappings.csv heat_load_profile_DK_AdamJensen.csv WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls Industrial_Database.csv retro/tabula-calculator-calcsetbuilding.csv
|
data % tar pczf pypsa-eur-sec-data-bundle.tar.gz eea/UNFCCC_v23.csv switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls Industrial_Database.csv retro/tabula-calculator-calcsetbuilding.csv nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson
|
||||||
|
|||||||
@ -44,11 +44,13 @@ Hydrogen network: nodal.
|
|||||||
Methane network: single node for Europe, since future demand is so
|
Methane network: single node for Europe, since future demand is so
|
||||||
low and no bottlenecks are expected.
|
low and no bottlenecks are expected.
|
||||||
|
|
||||||
Solid biomass: single node for Europe, until transport costs can be
|
Solid biomass: choice between single node for Europe and nodal where biomass
|
||||||
incorporated.
|
potential is regionally disaggregated (currently given per country,
|
||||||
|
then distributed by population density within)
|
||||||
|
and transport of solid biomass is possible.
|
||||||
|
|
||||||
CO2: single node for Europe, but a transport and storage cost is added for
|
CO2: single node for Europe, but a transport and storage cost is added for
|
||||||
sequestered CO2.
|
sequestered CO2. Optionally: nodal, with CO2 transport via pipelines.
|
||||||
|
|
||||||
Liquid hydrocarbons: single node for Europe, since transport costs for
|
Liquid hydrocarbons: single node for Europe, since transport costs for
|
||||||
liquids are low.
|
liquids are low.
|
||||||
|
|||||||
@ -43,7 +43,7 @@ Heat demand is split into:
|
|||||||
|
|
||||||
* ``urban central``: large-scale district heating networks in urban areas with dense heat demand
|
* ``urban central``: large-scale district heating networks in urban areas with dense heat demand
|
||||||
* ``residential/services urban decentral``: heating for individual buildings in urban areas
|
* ``residential/services urban decentral``: heating for individual buildings in urban areas
|
||||||
* ``residential/services rural``: heating for individual buildings in rural areas
|
* ``residential/services rural``: heating for individual buildings in rural areas, agriculture heat uses
|
||||||
|
|
||||||
|
|
||||||
Heat supply
|
Heat supply
|
||||||
@ -183,13 +183,13 @@ Solid biomass provides process heat up to 500 Celsius in industry, as well as fe
|
|||||||
Solid biomass supply
|
Solid biomass supply
|
||||||
=====================
|
=====================
|
||||||
|
|
||||||
Only wastes and residues from the JRC biomass dataset.
|
Only wastes and residues from the JRC ENSPRESO biomass dataset.
|
||||||
|
|
||||||
|
|
||||||
Oil product demand
|
Oil product demand
|
||||||
=====================
|
=====================
|
||||||
|
|
||||||
Transport fuels and naphtha as a feedstock for the chemicals industry.
|
Transport fuels, agriculture machinery and naphtha as a feedstock for the chemicals industry.
|
||||||
|
|
||||||
Oil product supply
|
Oil product supply
|
||||||
======================
|
======================
|
||||||
|
|||||||
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|
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|
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@ -28,7 +28,7 @@ def add_build_year_to_new_assets(n, baseyear):
|
|||||||
# Give assets with lifetimes and no build year the build year baseyear
|
# Give assets with lifetimes and no build year the build year baseyear
|
||||||
for c in n.iterate_components(["Link", "Generator", "Store"]):
|
for c in n.iterate_components(["Link", "Generator", "Store"]):
|
||||||
|
|
||||||
assets = c.df.index[~c.df.lifetime.isna() & c.df.build_year.isna()]
|
assets = c.df.index[~c.df.lifetime.isna() & c.df.build_year==0]
|
||||||
c.df.loc[assets, "build_year"] = baseyear
|
c.df.loc[assets, "build_year"] = baseyear
|
||||||
|
|
||||||
# add -baseyear to name
|
# add -baseyear to name
|
||||||
@ -155,6 +155,11 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
|||||||
# assign clustered bus
|
# assign clustered bus
|
||||||
busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0, squeeze=True)
|
busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0, squeeze=True)
|
||||||
busmap = pd.read_csv(snakemake.input.busmap, index_col=0, squeeze=True)
|
busmap = pd.read_csv(snakemake.input.busmap, index_col=0, squeeze=True)
|
||||||
|
|
||||||
|
inv_busmap = {}
|
||||||
|
for k, v in busmap.iteritems():
|
||||||
|
inv_busmap[v] = inv_busmap.get(v, []) + [k]
|
||||||
|
|
||||||
clustermaps = busmap_s.map(busmap)
|
clustermaps = busmap_s.map(busmap)
|
||||||
clustermaps.index = clustermaps.index.astype(int)
|
clustermaps.index = clustermaps.index.astype(int)
|
||||||
|
|
||||||
@ -193,8 +198,38 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
|||||||
|
|
||||||
if generator in ['solar', 'onwind', 'offwind']:
|
if generator in ['solar', 'onwind', 'offwind']:
|
||||||
|
|
||||||
rename = {"offwind": "offwind-ac"}
|
suffix = '-ac' if generator == 'offwind' else ''
|
||||||
p_max_pu=n.generators_t.p_max_pu[capacity.index + ' ' + rename.get(generator, generator) + '-' + str(baseyear)]
|
name_suffix = f' {generator}{suffix}-{baseyear}'
|
||||||
|
|
||||||
|
if 'm' in snakemake.wildcards.clusters:
|
||||||
|
|
||||||
|
for ind in capacity.index:
|
||||||
|
|
||||||
|
# existing capacities are split evenly among regions in every country
|
||||||
|
inv_ind = [i for i in inv_busmap[ind]]
|
||||||
|
|
||||||
|
# for offshore the spliting only inludes coastal regions
|
||||||
|
inv_ind = [i for i in inv_ind if (i + name_suffix) in n.generators.index]
|
||||||
|
|
||||||
|
p_max_pu = n.generators_t.p_max_pu[[i + name_suffix for i in inv_ind]]
|
||||||
|
p_max_pu.columns=[i + name_suffix for i in inv_ind ]
|
||||||
|
|
||||||
|
n.madd("Generator",
|
||||||
|
[i + name_suffix for i in inv_ind],
|
||||||
|
bus=ind,
|
||||||
|
carrier=generator,
|
||||||
|
p_nom=capacity[ind] / len(inv_ind), # split among regions in a country
|
||||||
|
marginal_cost=costs.at[generator,'VOM'],
|
||||||
|
capital_cost=costs.at[generator,'fixed'],
|
||||||
|
efficiency=costs.at[generator, 'efficiency'],
|
||||||
|
p_max_pu=p_max_pu,
|
||||||
|
build_year=grouping_year,
|
||||||
|
lifetime=costs.at[generator,'lifetime']
|
||||||
|
)
|
||||||
|
|
||||||
|
else:
|
||||||
|
|
||||||
|
p_max_pu = n.generators_t.p_max_pu[capacity.index + name_suffix]
|
||||||
|
|
||||||
n.madd("Generator",
|
n.madd("Generator",
|
||||||
capacity.index,
|
capacity.index,
|
||||||
@ -410,7 +445,8 @@ if __name__ == "__main__":
|
|||||||
simpl='',
|
simpl='',
|
||||||
clusters=45,
|
clusters=45,
|
||||||
lv=1.0,
|
lv=1.0,
|
||||||
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
|
opts='',
|
||||||
|
sector_opts='Co2L0-168H-T-H-B-I-solar+p3-dist1',
|
||||||
planning_horizons=2020,
|
planning_horizons=2020,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|||||||
@ -1,55 +1,194 @@
|
|||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
import geopandas as gpd
|
||||||
rename = {"UK" : "GB", "BH" : "BA"}
|
|
||||||
|
|
||||||
|
|
||||||
def build_biomass_potentials():
|
def build_nuts_population_data(year=2013):
|
||||||
|
|
||||||
config = snakemake.config['biomass']
|
pop = pd.read_csv(
|
||||||
year = config["year"]
|
snakemake.input.nuts3_population,
|
||||||
scenario = config["scenario"]
|
sep=r'\,| \t|\t',
|
||||||
|
engine='python',
|
||||||
|
na_values=[":"],
|
||||||
|
index_col=1
|
||||||
|
)[str(year)]
|
||||||
|
|
||||||
df = pd.read_excel(snakemake.input.jrc_potentials,
|
# only countries
|
||||||
"Potentials (PJ)",
|
pop.drop("EU28", inplace=True)
|
||||||
index_col=[0,1])
|
|
||||||
|
|
||||||
df.rename(columns={"Unnamed: 18": "Municipal waste"}, inplace=True)
|
# mapping from Cantons to NUTS3
|
||||||
df.drop(columns="Total", inplace=True)
|
cantons = pd.read_csv(snakemake.input.swiss_cantons)
|
||||||
df.replace("-", 0., inplace=True)
|
cantons = cantons.set_index(cantons.HASC.str[3:]).NUTS
|
||||||
|
cantons = cantons.str.pad(5, side='right', fillchar='0')
|
||||||
|
|
||||||
column = df.iloc[:,0]
|
# get population by NUTS3
|
||||||
countries = column.where(column.str.isalpha()).pad()
|
swiss = pd.read_excel(snakemake.input.swiss_population, skiprows=3, index_col=0).loc["Residents in 1000"]
|
||||||
countries = [rename.get(ct, ct) for ct in countries]
|
swiss = swiss.rename(cantons).filter(like="CH")
|
||||||
countries_i = pd.Index(countries, name='country')
|
|
||||||
df.set_index(countries_i, append=True, inplace=True)
|
|
||||||
|
|
||||||
df.drop(index='MS', level=0, inplace=True)
|
# aggregate also to higher order NUTS levels
|
||||||
|
swiss = [swiss.groupby(swiss.index.str[:i]).sum() for i in range(2, 6)]
|
||||||
|
|
||||||
# convert from PJ to MWh
|
# merge Europe + Switzerland
|
||||||
df = df / 3.6 * 1e6
|
pop = pd.DataFrame(pop.append(swiss), columns=["total"])
|
||||||
|
|
||||||
df.to_csv(snakemake.output.biomass_potentials_all)
|
# add missing manually
|
||||||
|
pop["AL"] = 2893
|
||||||
|
pop["BA"] = 3871
|
||||||
|
pop["RS"] = 7210
|
||||||
|
|
||||||
# solid biomass includes:
|
pop["ct"] = pop.index.str[:2]
|
||||||
# Primary agricultural residues (MINBIOAGRW1),
|
|
||||||
# Forestry energy residue (MINBIOFRSF1),
|
|
||||||
# Secondary forestry residues (MINBIOWOOW1),
|
|
||||||
# Secondary Forestry residues – sawdust (MINBIOWOO1a)',
|
|
||||||
# Forestry residues from landscape care biomass (MINBIOFRSF1a),
|
|
||||||
# Municipal waste (MINBIOMUN1)',
|
|
||||||
|
|
||||||
# biogas includes:
|
return pop
|
||||||
# Manure biomass potential (MINBIOGAS1),
|
|
||||||
# Sludge biomass (MINBIOSLU1),
|
|
||||||
|
|
||||||
df = df.loc[year, scenario, :]
|
|
||||||
|
|
||||||
grouper = {v: k for k, vv in config["classes"].items() for v in vv}
|
def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
|
||||||
df = df.groupby(grouper, axis=1).sum()
|
"""
|
||||||
|
Loads the JRC ENSPRESO biomass potentials.
|
||||||
|
|
||||||
df.index.name = "MWh/a"
|
Parameters
|
||||||
|
----------
|
||||||
|
year : int
|
||||||
|
The year for which potentials are to be taken.
|
||||||
|
Can be {2010, 2020, 2030, 2040, 2050}.
|
||||||
|
scenario : str
|
||||||
|
The scenario. Can be {"ENS_Low", "ENS_Med", "ENS_High"}.
|
||||||
|
|
||||||
df.to_csv(snakemake.output.biomass_potentials)
|
Returns
|
||||||
|
-------
|
||||||
|
pd.DataFrame
|
||||||
|
Biomass potentials for given year and scenario
|
||||||
|
in TWh/a by commodity and NUTS2 region.
|
||||||
|
"""
|
||||||
|
|
||||||
|
glossary = pd.read_excel(
|
||||||
|
str(snakemake.input.enspreso_biomass),
|
||||||
|
sheet_name="Glossary",
|
||||||
|
usecols="B:D",
|
||||||
|
skiprows=1,
|
||||||
|
index_col=0
|
||||||
|
)
|
||||||
|
|
||||||
|
df = pd.read_excel(
|
||||||
|
str(snakemake.input.enspreso_biomass),
|
||||||
|
sheet_name="ENER - NUTS2 BioCom E",
|
||||||
|
usecols="A:H"
|
||||||
|
)
|
||||||
|
|
||||||
|
df["group"] = df["E-Comm"].map(glossary.group)
|
||||||
|
df["commodity"] = df["E-Comm"].map(glossary.description)
|
||||||
|
|
||||||
|
to_rename = {
|
||||||
|
"NUTS2 Potential available by Bio Commodity": "potential",
|
||||||
|
"NUST2": "NUTS2",
|
||||||
|
}
|
||||||
|
df.rename(columns=to_rename, inplace=True)
|
||||||
|
|
||||||
|
# fill up with NUTS0 if NUTS2 is not given
|
||||||
|
df.NUTS2 = df.apply(lambda x: x.NUTS0 if x.NUTS2 == '-' else x.NUTS2, axis=1)
|
||||||
|
|
||||||
|
# convert PJ to TWh
|
||||||
|
df.potential /= 3.6
|
||||||
|
df.Unit = "TWh/a"
|
||||||
|
|
||||||
|
dff = df.query("Year == @year and Scenario == @scenario")
|
||||||
|
|
||||||
|
bio = dff.groupby(["NUTS2", "commodity"]).potential.sum().unstack()
|
||||||
|
|
||||||
|
# currently Serbia and Kosovo not split, so aggregate
|
||||||
|
bio.loc["RS"] += bio.loc["XK"]
|
||||||
|
bio.drop("XK", inplace=True)
|
||||||
|
|
||||||
|
return bio
|
||||||
|
|
||||||
|
|
||||||
|
def disaggregate_nuts0(bio):
|
||||||
|
"""
|
||||||
|
Some commodities are only given on NUTS0 level.
|
||||||
|
These are disaggregated here using the NUTS2
|
||||||
|
population as distribution key.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
bio : pd.DataFrame
|
||||||
|
from enspreso_biomass_potentials()
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
pd.DataFrame
|
||||||
|
"""
|
||||||
|
|
||||||
|
pop = build_nuts_population_data()
|
||||||
|
|
||||||
|
# get population in nuts2
|
||||||
|
pop_nuts2 = pop.loc[pop.index.str.len() == 4]
|
||||||
|
by_country = pop_nuts2.total.groupby(pop_nuts2.ct).sum()
|
||||||
|
pop_nuts2["fraction"] = pop_nuts2.total / pop_nuts2.ct.map(by_country)
|
||||||
|
|
||||||
|
# distribute nuts0 data to nuts2 by population
|
||||||
|
bio_nodal = bio.loc[pop_nuts2.ct]
|
||||||
|
bio_nodal.index = pop_nuts2.index
|
||||||
|
bio_nodal = bio_nodal.mul(pop_nuts2.fraction, axis=0)
|
||||||
|
|
||||||
|
# update inplace
|
||||||
|
bio.update(bio_nodal)
|
||||||
|
|
||||||
|
return bio
|
||||||
|
|
||||||
|
|
||||||
|
def build_nuts2_shapes():
|
||||||
|
"""
|
||||||
|
- load NUTS2 geometries
|
||||||
|
- add RS, AL, BA country shapes (not covered in NUTS 2013)
|
||||||
|
- consistently name ME, MK
|
||||||
|
"""
|
||||||
|
|
||||||
|
nuts2 = gpd.GeoDataFrame(gpd.read_file(snakemake.input.nuts2).set_index('id').geometry)
|
||||||
|
|
||||||
|
countries = gpd.read_file(snakemake.input.country_shapes).set_index('name')
|
||||||
|
missing = countries.loc[["AL", "RS", "BA"]]
|
||||||
|
nuts2.rename(index={"ME00": "ME", "MK00": "MK"}, inplace=True)
|
||||||
|
|
||||||
|
return nuts2.append(missing)
|
||||||
|
|
||||||
|
|
||||||
|
def area(gdf):
|
||||||
|
"""Returns area of GeoDataFrame geometries in square kilometers."""
|
||||||
|
return gdf.to_crs(epsg=3035).area.div(1e6)
|
||||||
|
|
||||||
|
|
||||||
|
def convert_nuts2_to_regions(bio_nuts2, regions):
|
||||||
|
"""
|
||||||
|
Converts biomass potentials given in NUTS2 to PyPSA-Eur regions based on the
|
||||||
|
overlay of both GeoDataFrames in proportion to the area.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
bio_nuts2 : gpd.GeoDataFrame
|
||||||
|
JRC ENSPRESO biomass potentials indexed by NUTS2 shapes.
|
||||||
|
regions : gpd.GeoDataFrame
|
||||||
|
PyPSA-Eur clustered onshore regions
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
gpd.GeoDataFrame
|
||||||
|
"""
|
||||||
|
|
||||||
|
# calculate area of nuts2 regions
|
||||||
|
bio_nuts2["area_nuts2"] = area(bio_nuts2)
|
||||||
|
|
||||||
|
overlay = gpd.overlay(regions, bio_nuts2)
|
||||||
|
|
||||||
|
# calculate share of nuts2 area inside region
|
||||||
|
overlay["share"] = area(overlay) / overlay["area_nuts2"]
|
||||||
|
|
||||||
|
# multiply all nuts2-level values with share of nuts2 inside region
|
||||||
|
adjust_cols = overlay.columns.difference({"name", "area_nuts2", "geometry", "share"})
|
||||||
|
overlay[adjust_cols] = overlay[adjust_cols].multiply(overlay["share"], axis=0)
|
||||||
|
|
||||||
|
bio_regions = overlay.groupby("name").sum()
|
||||||
|
|
||||||
|
bio_regions.drop(["area_nuts2", "share"], axis=1, inplace=True)
|
||||||
|
|
||||||
|
return bio_regions
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
@ -57,12 +196,28 @@ if __name__ == "__main__":
|
|||||||
from helper import mock_snakemake
|
from helper import mock_snakemake
|
||||||
snakemake = mock_snakemake('build_biomass_potentials')
|
snakemake = mock_snakemake('build_biomass_potentials')
|
||||||
|
|
||||||
|
config = snakemake.config['biomass']
|
||||||
|
year = config["year"]
|
||||||
|
scenario = config["scenario"]
|
||||||
|
|
||||||
# This is a hack, to be replaced once snakemake is unicode-conform
|
enspreso = enspreso_biomass_potentials(year, scenario)
|
||||||
|
|
||||||
solid_biomass = snakemake.config['biomass']['classes']['solid biomass']
|
enspreso = disaggregate_nuts0(enspreso)
|
||||||
if 'Secondary Forestry residues sawdust' in solid_biomass:
|
|
||||||
solid_biomass.remove('Secondary Forestry residues sawdust')
|
|
||||||
solid_biomass.append('Secondary Forestry residues – sawdust')
|
|
||||||
|
|
||||||
build_biomass_potentials()
|
nuts2 = build_nuts2_shapes()
|
||||||
|
|
||||||
|
df_nuts2 = gpd.GeoDataFrame(nuts2.geometry).join(enspreso)
|
||||||
|
|
||||||
|
regions = gpd.read_file(snakemake.input.regions_onshore)
|
||||||
|
|
||||||
|
df = convert_nuts2_to_regions(df_nuts2, regions)
|
||||||
|
|
||||||
|
df.to_csv(snakemake.output.biomass_potentials_all)
|
||||||
|
|
||||||
|
grouper = {v: k for k, vv in config["classes"].items() for v in vv}
|
||||||
|
df = df.groupby(grouper, axis=1).sum()
|
||||||
|
|
||||||
|
df *= 1e6 # TWh/a to MWh/a
|
||||||
|
df.index.name = "MWh/a"
|
||||||
|
|
||||||
|
df.to_csv(snakemake.output.biomass_potentials)
|
||||||
|
|||||||
90
scripts/build_biomass_transport_costs.py
Normal file
90
scripts/build_biomass_transport_costs.py
Normal file
@ -0,0 +1,90 @@
|
|||||||
|
"""
|
||||||
|
Reads biomass transport costs for different countries of the JRC report
|
||||||
|
|
||||||
|
"The JRC-EU-TIMES model.
|
||||||
|
Bioenergy potentials
|
||||||
|
for EU and neighbouring countries."
|
||||||
|
(2015)
|
||||||
|
|
||||||
|
converts them from units 'EUR per km/ton' -> 'EUR/ (km MWh)'
|
||||||
|
|
||||||
|
assuming as an approximation energy content of wood pellets
|
||||||
|
|
||||||
|
@author: bw0928
|
||||||
|
"""
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import tabula as tbl
|
||||||
|
|
||||||
|
ENERGY_CONTENT = 4.8 # unit MWh/t (wood pellets)
|
||||||
|
|
||||||
|
def get_countries():
|
||||||
|
|
||||||
|
pandas_options = dict(
|
||||||
|
skiprows=range(6),
|
||||||
|
header=None,
|
||||||
|
index_col=0
|
||||||
|
)
|
||||||
|
|
||||||
|
return tbl.read_pdf(
|
||||||
|
str(snakemake.input.transport_cost_data),
|
||||||
|
pages="145",
|
||||||
|
multiple_tables=False,
|
||||||
|
pandas_options=pandas_options
|
||||||
|
)[0].index
|
||||||
|
|
||||||
|
|
||||||
|
def get_cost_per_tkm(page, countries):
|
||||||
|
|
||||||
|
pandas_options = dict(
|
||||||
|
skiprows=range(6),
|
||||||
|
header=0,
|
||||||
|
sep=' |,',
|
||||||
|
engine='python',
|
||||||
|
index_col=False,
|
||||||
|
)
|
||||||
|
|
||||||
|
sc = tbl.read_pdf(
|
||||||
|
str(snakemake.input.transport_cost_data),
|
||||||
|
pages=page,
|
||||||
|
multiple_tables=False,
|
||||||
|
pandas_options=pandas_options
|
||||||
|
)[0]
|
||||||
|
sc.index = countries
|
||||||
|
sc.columns = sc.columns.str.replace("€", "EUR")
|
||||||
|
|
||||||
|
return sc
|
||||||
|
|
||||||
|
|
||||||
|
def build_biomass_transport_costs():
|
||||||
|
|
||||||
|
countries = get_countries()
|
||||||
|
|
||||||
|
sc1 = get_cost_per_tkm(146, countries)
|
||||||
|
sc2 = get_cost_per_tkm(147, countries)
|
||||||
|
|
||||||
|
# take mean of both supply chains
|
||||||
|
to_concat = [sc1["EUR/km/ton"], sc2["EUR/km/ton"]]
|
||||||
|
transport_costs = pd.concat(to_concat, axis=1).mean(axis=1)
|
||||||
|
|
||||||
|
# convert tonnes to MWh
|
||||||
|
transport_costs /= ENERGY_CONTENT
|
||||||
|
transport_costs.name = "EUR/km/MWh"
|
||||||
|
|
||||||
|
# rename country names
|
||||||
|
to_rename = {
|
||||||
|
"UK": "GB",
|
||||||
|
"XK": "KO",
|
||||||
|
"EL": "GR"
|
||||||
|
}
|
||||||
|
transport_costs.rename(to_rename, inplace=True)
|
||||||
|
|
||||||
|
# add missing Norway with data from Sweden
|
||||||
|
transport_costs["NO"] = transport_costs["SE"]
|
||||||
|
|
||||||
|
transport_costs.to_csv(snakemake.output[0])
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
|
||||||
|
build_biomass_transport_costs()
|
||||||
@ -117,6 +117,7 @@ to_ipcc = {
|
|||||||
"total energy": "1 - Energy",
|
"total energy": "1 - Energy",
|
||||||
"industrial processes": "2 - Industrial Processes and Product Use",
|
"industrial processes": "2 - Industrial Processes and Product Use",
|
||||||
"agriculture": "3 - Agriculture",
|
"agriculture": "3 - Agriculture",
|
||||||
|
"agriculture, forestry and fishing": '1.A.4.c - Agriculture/Forestry/Fishing',
|
||||||
"LULUCF": "4 - Land Use, Land-Use Change and Forestry",
|
"LULUCF": "4 - Land Use, Land-Use Change and Forestry",
|
||||||
"waste management": "5 - Waste management",
|
"waste management": "5 - Waste management",
|
||||||
"other": "6 - Other Sector",
|
"other": "6 - Other Sector",
|
||||||
@ -182,7 +183,7 @@ def idees_per_country(ct, year):
|
|||||||
|
|
||||||
ct_idees = idees_rename.get(ct, ct)
|
ct_idees = idees_rename.get(ct, ct)
|
||||||
fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
|
fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
|
||||||
fn_services = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
|
fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
|
||||||
fn_transport = f"{base_dir}/JRC-IDEES-2015_Transport_{ct_idees}.xlsx"
|
fn_transport = f"{base_dir}/JRC-IDEES-2015_Transport_{ct_idees}.xlsx"
|
||||||
|
|
||||||
# residential
|
# residential
|
||||||
@ -212,9 +213,15 @@ def idees_per_country(ct, year):
|
|||||||
assert df.index[47] == "Electricity"
|
assert df.index[47] == "Electricity"
|
||||||
ct_totals["electricity residential"] = df[47]
|
ct_totals["electricity residential"] = df[47]
|
||||||
|
|
||||||
|
assert df.index[46] == "Derived heat"
|
||||||
|
ct_totals["derived heat residential"] = df[46]
|
||||||
|
|
||||||
|
assert df.index[50] == 'Thermal uses'
|
||||||
|
ct_totals["thermal uses residential"] = df[50]
|
||||||
|
|
||||||
# services
|
# services
|
||||||
|
|
||||||
df = pd.read_excel(fn_services, "SER_hh_fec", index_col=0)[year]
|
df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)[year]
|
||||||
|
|
||||||
ct_totals["total services space"] = df["Space heating"]
|
ct_totals["total services space"] = df["Space heating"]
|
||||||
|
|
||||||
@ -231,7 +238,7 @@ def idees_per_country(ct, year):
|
|||||||
assert df.index[31] == "Electricity"
|
assert df.index[31] == "Electricity"
|
||||||
ct_totals["electricity services cooking"] = df[31]
|
ct_totals["electricity services cooking"] = df[31]
|
||||||
|
|
||||||
df = pd.read_excel(fn_services, "SER_summary", index_col=0)[year]
|
df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)[year]
|
||||||
|
|
||||||
row = "Energy consumption by fuel - Eurostat structure (ktoe)"
|
row = "Energy consumption by fuel - Eurostat structure (ktoe)"
|
||||||
ct_totals["total services"] = df[row]
|
ct_totals["total services"] = df[row]
|
||||||
@ -239,6 +246,41 @@ def idees_per_country(ct, year):
|
|||||||
assert df.index[50] == "Electricity"
|
assert df.index[50] == "Electricity"
|
||||||
ct_totals["electricity services"] = df[50]
|
ct_totals["electricity services"] = df[50]
|
||||||
|
|
||||||
|
assert df.index[49] == "Derived heat"
|
||||||
|
ct_totals["derived heat services"] = df[49]
|
||||||
|
|
||||||
|
assert df.index[53] == 'Thermal uses'
|
||||||
|
ct_totals["thermal uses services"] = df[53]
|
||||||
|
|
||||||
|
|
||||||
|
# agriculture, forestry and fishing
|
||||||
|
|
||||||
|
start = "Detailed split of energy consumption (ktoe)"
|
||||||
|
end = "Market shares of energy uses (%)"
|
||||||
|
|
||||||
|
df = pd.read_excel(fn_tertiary, "AGR_fec", index_col=0).loc[start:end, year]
|
||||||
|
|
||||||
|
rows = [
|
||||||
|
"Lighting",
|
||||||
|
"Ventilation",
|
||||||
|
"Specific electricity uses",
|
||||||
|
"Pumping devices (electric)"
|
||||||
|
]
|
||||||
|
ct_totals["total agriculture electricity"] = df[rows].sum()
|
||||||
|
|
||||||
|
rows = ["Specific heat uses", "Low enthalpy heat"]
|
||||||
|
ct_totals["total agriculture heat"] = df[rows].sum()
|
||||||
|
|
||||||
|
rows = [
|
||||||
|
"Motor drives",
|
||||||
|
"Farming machine drives (diesel oil incl. biofuels)",
|
||||||
|
"Pumping devices (diesel oil incl. biofuels)",
|
||||||
|
]
|
||||||
|
ct_totals["total agriculture machinery"] = df[rows].sum()
|
||||||
|
|
||||||
|
row = "Agriculture, forestry and fishing"
|
||||||
|
ct_totals["total agriculture"] = df[row]
|
||||||
|
|
||||||
# transport
|
# transport
|
||||||
|
|
||||||
df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)[year]
|
df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)[year]
|
||||||
@ -342,6 +384,7 @@ def build_idees(countries, year):
|
|||||||
with mp.Pool(processes=nprocesses) as pool:
|
with mp.Pool(processes=nprocesses) as pool:
|
||||||
totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
|
totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
|
||||||
|
|
||||||
|
|
||||||
totals = pd.concat(totals_list, axis=1)
|
totals = pd.concat(totals_list, axis=1)
|
||||||
|
|
||||||
# convert ktoe to TWh
|
# convert ktoe to TWh
|
||||||
@ -351,6 +394,13 @@ def build_idees(countries, year):
|
|||||||
# convert TWh/100km to kWh/km
|
# convert TWh/100km to kWh/km
|
||||||
totals.loc["passenger car efficiency"] *= 10
|
totals.loc["passenger car efficiency"] *= 10
|
||||||
|
|
||||||
|
# district heating share
|
||||||
|
district_heat = totals.loc[["derived heat residential",
|
||||||
|
"derived heat services"]].sum()
|
||||||
|
total_heat = totals.loc[["thermal uses residential",
|
||||||
|
"thermal uses services"]].sum()
|
||||||
|
totals.loc["district heat share"] = district_heat.div(total_heat)
|
||||||
|
|
||||||
return totals.T
|
return totals.T
|
||||||
|
|
||||||
|
|
||||||
@ -502,6 +552,14 @@ def build_energy_totals(countries, eurostat, swiss, idees):
|
|||||||
ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"]
|
ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"]
|
||||||
df.loc['BA', missing] = ratio * df.loc["RS", missing]
|
df.loc['BA', missing] = ratio * df.loc["RS", missing]
|
||||||
|
|
||||||
|
# Missing district heating share
|
||||||
|
dh_share = pd.read_csv(snakemake.input.district_heat_share,
|
||||||
|
index_col=0, usecols=[0, 1])
|
||||||
|
# make conservative assumption and take minimum from both data sets
|
||||||
|
df["district heat share"] = (pd.concat([df["district heat share"],
|
||||||
|
dh_share.reindex(index=df.index)/100],
|
||||||
|
axis=1).min(axis=1))
|
||||||
|
|
||||||
return df
|
return df
|
||||||
|
|
||||||
|
|
||||||
@ -540,10 +598,13 @@ def build_eea_co2(year=1990):
|
|||||||
"international aviation",
|
"international aviation",
|
||||||
"domestic navigation",
|
"domestic navigation",
|
||||||
"international navigation",
|
"international navigation",
|
||||||
|
"agriculture, forestry and fishing"
|
||||||
]
|
]
|
||||||
emissions["industrial non-elec"] = emissions["total energy"] - emissions[to_subtract].sum(axis=1)
|
emissions["industrial non-elec"] = emissions["total energy"] - emissions[to_subtract].sum(axis=1)
|
||||||
|
|
||||||
to_drop = ["total energy", "total wL", "total woL"]
|
emissions["agriculture"] += emissions["agriculture, forestry and fishing"]
|
||||||
|
|
||||||
|
to_drop = ["total energy", "total wL", "total woL", "agriculture, forestry and fishing"]
|
||||||
emissions.drop(columns=to_drop, inplace=True)
|
emissions.drop(columns=to_drop, inplace=True)
|
||||||
|
|
||||||
# convert from Gg to Mt
|
# convert from Gg to Mt
|
||||||
@ -588,7 +649,7 @@ def build_co2_totals(countries, eea_co2, eurostat_co2):
|
|||||||
# does not include industrial process emissions or fuel processing/refining
|
# does not include industrial process emissions or fuel processing/refining
|
||||||
"industrial non-elec": (ct, "+", "Industry"),
|
"industrial non-elec": (ct, "+", "Industry"),
|
||||||
# does not include non-energy emissions
|
# does not include non-energy emissions
|
||||||
"agriculture": (ct, "+", "+", "Agriculture / Forestry"),
|
"agriculture": (eurostat_co2.index.get_level_values(0) == ct) & eurostat_co2.index.isin(["Agriculture / Forestry", "Fishing"], level=3),
|
||||||
}
|
}
|
||||||
|
|
||||||
for i, mi in mappings.items():
|
for i, mi in mappings.items():
|
||||||
|
|||||||
@ -103,6 +103,7 @@ def add_ammonia_energy_demand(demand):
|
|||||||
demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - demand["Ammonia"]
|
demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - demand["Ammonia"]
|
||||||
|
|
||||||
demand['Basic chemicals (without ammonia)'].clip(lower=0, inplace=True)
|
demand['Basic chemicals (without ammonia)'].clip(lower=0, inplace=True)
|
||||||
|
|
||||||
demand.drop(columns='Basic chemicals', inplace=True)
|
demand.drop(columns='Basic chemicals', inplace=True)
|
||||||
|
|
||||||
return demand
|
return demand
|
||||||
@ -114,6 +115,11 @@ def add_non_eu28_industrial_energy_demand(demand):
|
|||||||
fn = snakemake.input.industrial_production_per_country
|
fn = snakemake.input.industrial_production_per_country
|
||||||
production = pd.read_csv(fn, index_col=0) / 1e3
|
production = pd.read_csv(fn, index_col=0) / 1e3
|
||||||
|
|
||||||
|
#recombine HVC, Chlorine and Methanol to Basic chemicals (without ammonia)
|
||||||
|
chemicals = ["HVC", "Chlorine", "Methanol"]
|
||||||
|
production["Basic chemicals (without ammonia)"] = production[chemicals].sum(axis=1)
|
||||||
|
production.drop(columns=chemicals, inplace=True)
|
||||||
|
|
||||||
eu28_production = production.loc[eu28].sum()
|
eu28_production = production.loc[eu28].sum()
|
||||||
eu28_energy = demand.groupby(level=1).sum()
|
eu28_energy = demand.groupby(level=1).sum()
|
||||||
eu28_averages = eu28_energy / eu28_production
|
eu28_averages = eu28_energy / eu28_production
|
||||||
|
|||||||
@ -179,8 +179,8 @@ def industry_production(countries):
|
|||||||
return demand
|
return demand
|
||||||
|
|
||||||
|
|
||||||
def add_ammonia_demand_separately(demand):
|
def separate_basic_chemicals(demand):
|
||||||
"""Include ammonia demand separately and remove ammonia from basic chemicals."""
|
"""Separate basic chemicals into ammonia, chlorine, methanol and HVC."""
|
||||||
|
|
||||||
ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
|
ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
|
||||||
|
|
||||||
@ -189,7 +189,7 @@ def add_ammonia_demand_separately(demand):
|
|||||||
|
|
||||||
print("Following countries have no ammonia demand:", missing)
|
print("Following countries have no ammonia demand:", missing)
|
||||||
|
|
||||||
demand.insert(2, "Ammonia", 0.)
|
demand["Ammonia"] = 0.
|
||||||
|
|
||||||
demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year)]
|
demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year)]
|
||||||
|
|
||||||
@ -198,9 +198,13 @@ def add_ammonia_demand_separately(demand):
|
|||||||
# EE, HR and LT got negative demand through subtraction - poor data
|
# EE, HR and LT got negative demand through subtraction - poor data
|
||||||
demand['Basic chemicals'].clip(lower=0., inplace=True)
|
demand['Basic chemicals'].clip(lower=0., inplace=True)
|
||||||
|
|
||||||
to_rename = {"Basic chemicals": "Basic chemicals (without ammonia)"}
|
# assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
|
||||||
demand.rename(columns=to_rename, inplace=True)
|
distribution_key = demand["Basic chemicals"] / demand["Basic chemicals"].sum()
|
||||||
|
demand["HVC"] = config["HVC_production_today"] * 1e3 * distribution_key
|
||||||
|
demand["Chlorine"] = config["chlorine_production_today"] * 1e3 * distribution_key
|
||||||
|
demand["Methanol"] = config["methanol_production_today"] * 1e3 * distribution_key
|
||||||
|
|
||||||
|
demand.drop(columns=["Basic chemicals"], inplace=True)
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
if 'snakemake' not in globals():
|
if 'snakemake' not in globals():
|
||||||
@ -211,12 +215,14 @@ if __name__ == '__main__':
|
|||||||
|
|
||||||
year = snakemake.config['industry']['reference_year']
|
year = snakemake.config['industry']['reference_year']
|
||||||
|
|
||||||
|
config = snakemake.config["industry"]
|
||||||
|
|
||||||
jrc_dir = snakemake.input.jrc
|
jrc_dir = snakemake.input.jrc
|
||||||
eurostat_dir = snakemake.input.eurostat
|
eurostat_dir = snakemake.input.eurostat
|
||||||
|
|
||||||
demand = industry_production(countries)
|
demand = industry_production(countries)
|
||||||
|
|
||||||
add_ammonia_demand_separately(demand)
|
separate_basic_chemicals(demand)
|
||||||
|
|
||||||
fn = snakemake.output.industrial_production_per_country
|
fn = snakemake.output.industrial_production_per_country
|
||||||
demand.to_csv(fn, float_format='%.2f')
|
demand.to_csv(fn, float_format='%.2f')
|
||||||
|
|||||||
@ -2,6 +2,8 @@
|
|||||||
|
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
|
||||||
|
from prepare_sector_network import get
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
if 'snakemake' not in globals():
|
if 'snakemake' not in globals():
|
||||||
from helper import mock_snakemake
|
from helper import mock_snakemake
|
||||||
@ -9,31 +11,42 @@ if __name__ == '__main__':
|
|||||||
|
|
||||||
config = snakemake.config["industry"]
|
config = snakemake.config["industry"]
|
||||||
|
|
||||||
|
investment_year = int(snakemake.wildcards.planning_horizons)
|
||||||
|
|
||||||
fn = snakemake.input.industrial_production_per_country
|
fn = snakemake.input.industrial_production_per_country
|
||||||
production = pd.read_csv(fn, index_col=0)
|
production = pd.read_csv(fn, index_col=0)
|
||||||
|
|
||||||
keys = ["Integrated steelworks", "Electric arc"]
|
keys = ["Integrated steelworks", "Electric arc"]
|
||||||
total_steel = production[keys].sum(axis=1)
|
total_steel = production[keys].sum(axis=1)
|
||||||
|
|
||||||
|
st_primary_fraction = get(config["St_primary_fraction"], investment_year)
|
||||||
|
dri_fraction = get(config["DRI_fraction"], investment_year)
|
||||||
int_steel = production["Integrated steelworks"].sum()
|
int_steel = production["Integrated steelworks"].sum()
|
||||||
fraction_persistent_primary = config["St_primary_fraction"] * total_steel.sum() / int_steel
|
fraction_persistent_primary = st_primary_fraction * total_steel.sum() / int_steel
|
||||||
|
|
||||||
dri = fraction_persistent_primary * production["Integrated steelworks"]
|
dri = dri_fraction * fraction_persistent_primary * production["Integrated steelworks"]
|
||||||
production.insert(2, "DRI + Electric arc", dri)
|
production.insert(2, "DRI + Electric arc", dri)
|
||||||
|
|
||||||
production["Electric arc"] = total_steel - production["DRI + Electric arc"]
|
not_dri = (1 - dri_fraction)
|
||||||
production["Integrated steelworks"] = 0.
|
production["Integrated steelworks"] = not_dri * fraction_persistent_primary * production["Integrated steelworks"]
|
||||||
|
production["Electric arc"] = total_steel - production["DRI + Electric arc"] - production["Integrated steelworks"]
|
||||||
|
|
||||||
keys = ["Aluminium - primary production", "Aluminium - secondary production"]
|
keys = ["Aluminium - primary production", "Aluminium - secondary production"]
|
||||||
total_aluminium = production[keys].sum(axis=1)
|
total_aluminium = production[keys].sum(axis=1)
|
||||||
|
|
||||||
key_pri = "Aluminium - primary production"
|
key_pri = "Aluminium - primary production"
|
||||||
key_sec = "Aluminium - secondary production"
|
key_sec = "Aluminium - secondary production"
|
||||||
fraction_persistent_primary = config["Al_primary_fraction"] * total_aluminium.sum() / production[key_pri].sum()
|
|
||||||
|
al_primary_fraction = get(config["Al_primary_fraction"], investment_year)
|
||||||
|
fraction_persistent_primary = al_primary_fraction * total_aluminium.sum() / production[key_pri].sum()
|
||||||
|
|
||||||
production[key_pri] = fraction_persistent_primary * production[key_pri]
|
production[key_pri] = fraction_persistent_primary * production[key_pri]
|
||||||
production[key_sec] = total_aluminium - production[key_pri]
|
production[key_sec] = total_aluminium - production[key_pri]
|
||||||
|
|
||||||
production["Basic chemicals (without ammonia)"] *= config['HVC_primary_fraction']
|
production["HVC (mechanical recycling)"] = get(config["HVC_mechanical_recycling_fraction"], investment_year) * production["HVC"]
|
||||||
|
production["HVC (chemical recycling)"] = get(config["HVC_chemical_recycling_fraction"], investment_year) * production["HVC"]
|
||||||
|
|
||||||
|
production["HVC"] *= get(config['HVC_primary_fraction'], investment_year)
|
||||||
|
|
||||||
fn = snakemake.output.industrial_production_per_country_tomorrow
|
fn = snakemake.output.industrial_production_per_country_tomorrow
|
||||||
production.to_csv(fn, float_format='%.2f')
|
production.to_csv(fn, float_format='%.2f')
|
||||||
|
|||||||
@ -9,7 +9,11 @@ sector_mapping = {
|
|||||||
'Integrated steelworks': 'Iron and steel',
|
'Integrated steelworks': 'Iron and steel',
|
||||||
'DRI + Electric arc': 'Iron and steel',
|
'DRI + Electric arc': 'Iron and steel',
|
||||||
'Ammonia': 'Chemical industry',
|
'Ammonia': 'Chemical industry',
|
||||||
'Basic chemicals (without ammonia)': 'Chemical industry',
|
'HVC': 'Chemical industry',
|
||||||
|
'HVC (mechanical recycling)': 'Chemical industry',
|
||||||
|
'HVC (chemical recycling)': 'Chemical industry',
|
||||||
|
'Methanol': 'Chemical industry',
|
||||||
|
'Chlorine': 'Chemical industry',
|
||||||
'Other chemicals': 'Chemical industry',
|
'Other chemicals': 'Chemical industry',
|
||||||
'Pharmaceutical products etc.': 'Chemical industry',
|
'Pharmaceutical products etc.': 'Chemical industry',
|
||||||
'Cement': 'Cement',
|
'Cement': 'Cement',
|
||||||
|
|||||||
@ -279,7 +279,7 @@ def chemicals_industry():
|
|||||||
|
|
||||||
df = pd.DataFrame(index=index)
|
df = pd.DataFrame(index=index)
|
||||||
|
|
||||||
# Basid chemicals
|
# Basic chemicals
|
||||||
|
|
||||||
sector = "Basic chemicals"
|
sector = "Basic chemicals"
|
||||||
|
|
||||||
@ -374,52 +374,82 @@ def chemicals_industry():
|
|||||||
# putting in ammonia demand for H2 and electricity separately
|
# putting in ammonia demand for H2 and electricity separately
|
||||||
|
|
||||||
s_emi = idees["emi"][3:57]
|
s_emi = idees["emi"][3:57]
|
||||||
s_out = idees["out"][8:9]
|
|
||||||
assert s_emi.index[0] == sector
|
assert s_emi.index[0] == sector
|
||||||
assert sector in str(s_out.index)
|
|
||||||
|
|
||||||
ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
|
# convert from MtHVC/a to ktHVC/a
|
||||||
|
s_out = config["HVC_production_today"] * 1e3
|
||||||
# ktNH3/a
|
|
||||||
ammonia_total = ammonia.loc[ammonia.index.intersection(eu28), str(year)].sum()
|
|
||||||
|
|
||||||
s_out -= ammonia_total
|
|
||||||
|
|
||||||
# tCO2/t material
|
# tCO2/t material
|
||||||
df.loc["process emission", sector] += (
|
df.loc["process emission", sector] += (
|
||||||
s_emi["Process emissions"]
|
s_emi["Process emissions"]
|
||||||
- config["petrochemical_process_emissions"] * 1e3
|
- config["petrochemical_process_emissions"] * 1e3
|
||||||
- config["NH3_process_emissions"] * 1e3
|
- config["NH3_process_emissions"] * 1e3
|
||||||
) / s_out.values
|
) / s_out
|
||||||
|
|
||||||
# emissions originating from feedstock, could be non-fossil origin
|
# emissions originating from feedstock, could be non-fossil origin
|
||||||
# tCO2/t material
|
# tCO2/t material
|
||||||
df.loc["process emission from feedstock", sector] += (
|
df.loc["process emission from feedstock", sector] += (
|
||||||
config["petrochemical_process_emissions"] * 1e3
|
config["petrochemical_process_emissions"] * 1e3
|
||||||
) / s_out.values
|
) / s_out
|
||||||
|
|
||||||
# convert from ktoe/a to GWh/a
|
# convert from ktoe/a to GWh/a
|
||||||
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
|
||||||
df.loc[sources, sector] *= toe_to_MWh
|
df.loc[sources, sector] *= toe_to_MWh
|
||||||
|
|
||||||
|
# subtract ammonia energy demand (in ktNH3/a)
|
||||||
|
ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
|
||||||
|
ammonia_total = ammonia.loc[ammonia.index.intersection(eu28), str(year)].sum()
|
||||||
df.loc["methane", sector] -= ammonia_total * config["MWh_CH4_per_tNH3_SMR"]
|
df.loc["methane", sector] -= ammonia_total * config["MWh_CH4_per_tNH3_SMR"]
|
||||||
df.loc["elec", sector] -= ammonia_total * config["MWh_elec_per_tNH3_SMR"]
|
df.loc["elec", sector] -= ammonia_total * config["MWh_elec_per_tNH3_SMR"]
|
||||||
|
|
||||||
# MWh/t material
|
# subtract chlorine demand
|
||||||
df.loc[sources, sector] = df.loc[sources, sector] / s_out.values
|
chlorine_total = config["chlorine_production_today"]
|
||||||
|
df.loc["hydrogen", sector] -= chlorine_total * config["MWh_H2_per_tCl"]
|
||||||
|
df.loc["elec", sector] -= chlorine_total * config["MWh_elec_per_tCl"]
|
||||||
|
|
||||||
to_rename = {sector: f"{sector} (without ammonia)"}
|
# subtract methanol demand
|
||||||
df.rename(columns=to_rename, inplace=True)
|
methanol_total = config["methanol_production_today"]
|
||||||
|
df.loc["methane", sector] -= methanol_total * config["MWh_CH4_per_tMeOH"]
|
||||||
|
df.loc["elec", sector] -= methanol_total * config["MWh_elec_per_tMeOH"]
|
||||||
|
|
||||||
|
# MWh/t material
|
||||||
|
df.loc[sources, sector] = df.loc[sources, sector] / s_out
|
||||||
|
|
||||||
|
df.rename(columns={sector: "HVC"}, inplace=True)
|
||||||
|
|
||||||
|
# HVC mechanical recycling
|
||||||
|
|
||||||
|
sector = "HVC (mechanical recycling)"
|
||||||
|
df[sector] = 0.0
|
||||||
|
df.loc["elec", sector] = config["MWh_elec_per_tHVC_mechanical_recycling"]
|
||||||
|
|
||||||
|
# HVC chemical recycling
|
||||||
|
|
||||||
|
sector = "HVC (chemical recycling)"
|
||||||
|
df[sector] = 0.0
|
||||||
|
df.loc["elec", sector] = config["MWh_elec_per_tHVC_chemical_recycling"]
|
||||||
|
|
||||||
# Ammonia
|
# Ammonia
|
||||||
|
|
||||||
sector = "Ammonia"
|
sector = "Ammonia"
|
||||||
|
|
||||||
df[sector] = 0.0
|
df[sector] = 0.0
|
||||||
|
|
||||||
df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"]
|
df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"]
|
||||||
df.loc["elec", sector] = config["MWh_elec_per_tNH3_electrolysis"]
|
df.loc["elec", sector] = config["MWh_elec_per_tNH3_electrolysis"]
|
||||||
|
|
||||||
|
# Chlorine
|
||||||
|
|
||||||
|
sector = "Chlorine"
|
||||||
|
df[sector] = 0.0
|
||||||
|
df.loc["hydrogen", sector] = config["MWh_H2_per_tCl"]
|
||||||
|
df.loc["elec", sector] = config["MWh_elec_per_tCl"]
|
||||||
|
|
||||||
|
# Methanol
|
||||||
|
|
||||||
|
sector = "Methanol"
|
||||||
|
df[sector] = 0.0
|
||||||
|
df.loc["methane", sector] = config["MWh_CH4_per_tMeOH"]
|
||||||
|
df.loc["elec", sector] = config["MWh_elec_per_tMeOH"]
|
||||||
|
|
||||||
# Other chemicals
|
# Other chemicals
|
||||||
|
|
||||||
sector = "Other chemicals"
|
sector = "Other chemicals"
|
||||||
|
|||||||
@ -90,8 +90,8 @@ if __name__ == '__main__':
|
|||||||
|
|
||||||
for key, pop in pop_cells.items():
|
for key, pop in pop_cells.items():
|
||||||
|
|
||||||
ycoords = ('y', cutout.coords['y'])
|
ycoords = ('y', cutout.coords['y'].data)
|
||||||
xcoords = ('x', cutout.coords['x'])
|
xcoords = ('x', cutout.coords['x'].data)
|
||||||
values = pop.values.reshape(cutout.shape)
|
values = pop.values.reshape(cutout.shape)
|
||||||
layout = xr.DataArray(values, [ycoords, xcoords])
|
layout = xr.DataArray(values, [ycoords, xcoords])
|
||||||
|
|
||||||
|
|||||||
@ -5,7 +5,8 @@ files = [
|
|||||||
"config.yaml",
|
"config.yaml",
|
||||||
"Snakefile",
|
"Snakefile",
|
||||||
"scripts/solve_network.py",
|
"scripts/solve_network.py",
|
||||||
"scripts/prepare_sector_network.py"
|
"scripts/prepare_sector_network.py",
|
||||||
|
"../pypsa-eur/config.yaml"
|
||||||
]
|
]
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
|
|||||||
@ -19,9 +19,11 @@ def rename_techs_tyndp(tech):
|
|||||||
tech = rename_techs(tech)
|
tech = rename_techs(tech)
|
||||||
if "heat pump" in tech or "resistive heater" in tech:
|
if "heat pump" in tech or "resistive heater" in tech:
|
||||||
return "power-to-heat"
|
return "power-to-heat"
|
||||||
elif tech in ["methanation", "hydrogen storage", "helmeth"]:
|
elif tech in ["H2 Electrolysis", "methanation", "helmeth", "H2 liquefaction"]:
|
||||||
return "power-to-gas"
|
return "power-to-gas"
|
||||||
elif tech in ["OCGT", "CHP", "gas boiler"]:
|
elif tech == "H2":
|
||||||
|
return "H2 storage"
|
||||||
|
elif tech in ["OCGT", "CHP", "gas boiler", "H2 Fuel Cell"]:
|
||||||
return "gas-to-power/heat"
|
return "gas-to-power/heat"
|
||||||
elif "solar" in tech:
|
elif "solar" in tech:
|
||||||
return "solar"
|
return "solar"
|
||||||
@ -29,6 +31,8 @@ def rename_techs_tyndp(tech):
|
|||||||
return "power-to-liquid"
|
return "power-to-liquid"
|
||||||
elif "offshore wind" in tech:
|
elif "offshore wind" in tech:
|
||||||
return "offshore wind"
|
return "offshore wind"
|
||||||
|
elif "CC" in tech or "sequestration" in tech:
|
||||||
|
return "CCS"
|
||||||
else:
|
else:
|
||||||
return tech
|
return tech
|
||||||
|
|
||||||
@ -286,7 +290,7 @@ def plot_h2_map(network):
|
|||||||
l2 = ax.legend(
|
l2 = ax.legend(
|
||||||
handles, labels,
|
handles, labels,
|
||||||
loc="upper left",
|
loc="upper left",
|
||||||
bbox_to_anchor=(0.01, 1.01),
|
bbox_to_anchor=(-0.03, 1.01),
|
||||||
labelspacing=1.0,
|
labelspacing=1.0,
|
||||||
frameon=False,
|
frameon=False,
|
||||||
title='Electrolyzer capacity',
|
title='Electrolyzer capacity',
|
||||||
@ -662,7 +666,8 @@ def plot_series(network, carrier="AC", name="test"):
|
|||||||
|
|
||||||
supply = pd.DataFrame(index=n.snapshots)
|
supply = pd.DataFrame(index=n.snapshots)
|
||||||
for c in n.iterate_components(n.branch_components):
|
for c in n.iterate_components(n.branch_components):
|
||||||
for i in range(2):
|
n_port = 4 if c.name=='Link' else 2
|
||||||
|
for i in range(n_port):
|
||||||
supply = pd.concat((supply,
|
supply = pd.concat((supply,
|
||||||
(-1) * c.pnl["p" + str(i)].loc[:,
|
(-1) * c.pnl["p" + str(i)].loc[:,
|
||||||
c.df.index[c.df["bus" + str(i)].isin(buses)]].groupby(c.df.carrier,
|
c.df.index[c.df["bus" + str(i)].isin(buses)]].groupby(c.df.carrier,
|
||||||
@ -831,10 +836,11 @@ if __name__ == "__main__":
|
|||||||
snakemake = mock_snakemake(
|
snakemake = mock_snakemake(
|
||||||
'plot_network',
|
'plot_network',
|
||||||
simpl='',
|
simpl='',
|
||||||
clusters=48,
|
clusters=45,
|
||||||
lv=1.0,
|
lv=1.5,
|
||||||
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
|
opts='',
|
||||||
planning_horizons=2050,
|
sector_opts='Co2L0-168H-T-H-B-I-solar+p3-dist1',
|
||||||
|
planning_horizons=2030,
|
||||||
)
|
)
|
||||||
|
|
||||||
overrides = override_component_attrs(snakemake.input.overrides)
|
overrides = override_component_attrs(snakemake.input.overrides)
|
||||||
|
|||||||
@ -34,9 +34,11 @@ def rename_techs(label):
|
|||||||
rename_if_contains_dict = {
|
rename_if_contains_dict = {
|
||||||
"water tanks": "hot water storage",
|
"water tanks": "hot water storage",
|
||||||
"retrofitting": "building retrofitting",
|
"retrofitting": "building retrofitting",
|
||||||
"H2": "hydrogen storage",
|
# "H2 Electrolysis": "hydrogen storage",
|
||||||
|
# "H2 Fuel Cell": "hydrogen storage",
|
||||||
|
# "H2 pipeline": "hydrogen storage",
|
||||||
"battery": "battery storage",
|
"battery": "battery storage",
|
||||||
"CC": "CC"
|
# "CC": "CC"
|
||||||
}
|
}
|
||||||
|
|
||||||
rename = {
|
rename = {
|
||||||
@ -88,6 +90,7 @@ preferred_order = pd.Index([
|
|||||||
"offshore wind (DC)",
|
"offshore wind (DC)",
|
||||||
"solar PV",
|
"solar PV",
|
||||||
"solar thermal",
|
"solar thermal",
|
||||||
|
"solar rooftop",
|
||||||
"solar",
|
"solar",
|
||||||
"building retrofitting",
|
"building retrofitting",
|
||||||
"ground heat pump",
|
"ground heat pump",
|
||||||
|
|||||||
@ -19,7 +19,6 @@ from helper import override_component_attrs
|
|||||||
import logging
|
import logging
|
||||||
logger = logging.getLogger(__name__)
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
from types import SimpleNamespace
|
from types import SimpleNamespace
|
||||||
spatial = SimpleNamespace()
|
spatial = SimpleNamespace()
|
||||||
|
|
||||||
@ -38,6 +37,40 @@ def define_spatial(nodes):
|
|||||||
|
|
||||||
spatial.nodes = nodes
|
spatial.nodes = nodes
|
||||||
|
|
||||||
|
# biomass
|
||||||
|
|
||||||
|
spatial.biomass = SimpleNamespace()
|
||||||
|
|
||||||
|
if options["biomass_transport"]:
|
||||||
|
spatial.biomass.nodes = nodes + " solid biomass"
|
||||||
|
spatial.biomass.locations = nodes
|
||||||
|
spatial.biomass.industry = nodes + " solid biomass for industry"
|
||||||
|
spatial.biomass.industry_cc = nodes + " solid biomass for industry CC"
|
||||||
|
else:
|
||||||
|
spatial.biomass.nodes = ["EU solid biomass"]
|
||||||
|
spatial.biomass.locations = ["EU"]
|
||||||
|
spatial.biomass.industry = ["solid biomass for industry"]
|
||||||
|
spatial.biomass.industry_cc = ["solid biomass for industry CC"]
|
||||||
|
|
||||||
|
spatial.biomass.df = pd.DataFrame(vars(spatial.biomass), index=nodes)
|
||||||
|
|
||||||
|
# co2
|
||||||
|
|
||||||
|
spatial.co2 = SimpleNamespace()
|
||||||
|
|
||||||
|
if options["co2_network"]:
|
||||||
|
spatial.co2.nodes = nodes + " co2 stored"
|
||||||
|
spatial.co2.locations = nodes
|
||||||
|
spatial.co2.vents = nodes + " co2 vent"
|
||||||
|
else:
|
||||||
|
spatial.co2.nodes = ["co2 stored"]
|
||||||
|
spatial.co2.locations = ["EU"]
|
||||||
|
spatial.co2.vents = ["co2 vent"]
|
||||||
|
|
||||||
|
spatial.co2.df = pd.DataFrame(vars(spatial.co2), index=nodes)
|
||||||
|
|
||||||
|
# gas
|
||||||
|
|
||||||
spatial.gas = SimpleNamespace()
|
spatial.gas = SimpleNamespace()
|
||||||
|
|
||||||
if options["gas_network"]:
|
if options["gas_network"]:
|
||||||
@ -56,6 +89,10 @@ def define_spatial(nodes):
|
|||||||
spatial.gas.df = pd.DataFrame(vars(spatial.gas), index=nodes)
|
spatial.gas.df = pd.DataFrame(vars(spatial.gas), index=nodes)
|
||||||
|
|
||||||
|
|
||||||
|
from types import SimpleNamespace
|
||||||
|
spatial = SimpleNamespace()
|
||||||
|
|
||||||
|
|
||||||
def emission_sectors_from_opts(opts):
|
def emission_sectors_from_opts(opts):
|
||||||
|
|
||||||
sectors = ["electricity"]
|
sectors = ["electricity"]
|
||||||
@ -78,6 +115,10 @@ def emission_sectors_from_opts(opts):
|
|||||||
"domestic navigation",
|
"domestic navigation",
|
||||||
"international navigation"
|
"international navigation"
|
||||||
]
|
]
|
||||||
|
if "A" in opts:
|
||||||
|
sectors += [
|
||||||
|
"agriculture"
|
||||||
|
]
|
||||||
|
|
||||||
return sectors
|
return sectors
|
||||||
|
|
||||||
@ -90,6 +131,40 @@ def get(item, investment_year=None):
|
|||||||
return item
|
return item
|
||||||
|
|
||||||
|
|
||||||
|
def create_network_topology(n, prefix, connector=" -> "):
|
||||||
|
"""
|
||||||
|
Create a network topology like the power transmission network.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
n : pypsa.Network
|
||||||
|
prefix : str
|
||||||
|
connector : str
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
pd.DataFrame with columns bus0, bus1 and length
|
||||||
|
"""
|
||||||
|
|
||||||
|
ln_attrs = ["bus0", "bus1", "length"]
|
||||||
|
lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
|
||||||
|
|
||||||
|
candidates = pd.concat([
|
||||||
|
n.lines[ln_attrs],
|
||||||
|
n.links.loc[n.links.carrier == "DC", lk_attrs]
|
||||||
|
]).fillna(0)
|
||||||
|
|
||||||
|
positive_order = candidates.bus0 < candidates.bus1
|
||||||
|
candidates_p = candidates[positive_order]
|
||||||
|
swap_buses = {"bus0": "bus1", "bus1": "bus0"}
|
||||||
|
candidates_n = candidates[~positive_order].rename(columns=swap_buses)
|
||||||
|
candidates = pd.concat([candidates_p, candidates_n])
|
||||||
|
|
||||||
|
topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean()
|
||||||
|
topo.index = topo.apply(lambda c: prefix + c.bus0 + connector + c.bus1, axis=1)
|
||||||
|
return topo
|
||||||
|
|
||||||
|
|
||||||
def co2_emissions_year(countries, opts, year):
|
def co2_emissions_year(countries, opts, year):
|
||||||
"""
|
"""
|
||||||
Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
|
Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
|
||||||
@ -139,9 +214,6 @@ def build_carbon_budget(o, fn):
|
|||||||
#emissions at the beginning of the path (last year available 2018)
|
#emissions at the beginning of the path (last year available 2018)
|
||||||
e_0 = co2_emissions_year(countries, opts, year=2018)
|
e_0 = co2_emissions_year(countries, opts, year=2018)
|
||||||
|
|
||||||
#emissions in 2019 and 2020 assumed equal to 2018 and substracted
|
|
||||||
carbon_budget -= 2 * e_0
|
|
||||||
|
|
||||||
planning_horizons = snakemake.config['scenario']['planning_horizons']
|
planning_horizons = snakemake.config['scenario']['planning_horizons']
|
||||||
t_0 = planning_horizons[0]
|
t_0 = planning_horizons[0]
|
||||||
|
|
||||||
@ -180,6 +252,53 @@ def add_lifetime_wind_solar(n, costs):
|
|||||||
n.generators.loc[gen_i, "lifetime"] = costs.at[carrier, 'lifetime']
|
n.generators.loc[gen_i, "lifetime"] = costs.at[carrier, 'lifetime']
|
||||||
|
|
||||||
|
|
||||||
|
def create_network_topology(n, prefix, connector=" -> ", bidirectional=True):
|
||||||
|
"""
|
||||||
|
Create a network topology like the power transmission network.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
n : pypsa.Network
|
||||||
|
prefix : str
|
||||||
|
connector : str
|
||||||
|
bidirectional : bool, default True
|
||||||
|
True: one link for each connection
|
||||||
|
False: one link for each connection and direction (back and forth)
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
pd.DataFrame with columns bus0, bus1 and length
|
||||||
|
"""
|
||||||
|
|
||||||
|
ln_attrs = ["bus0", "bus1", "length"]
|
||||||
|
lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
|
||||||
|
|
||||||
|
candidates = pd.concat([
|
||||||
|
n.lines[ln_attrs],
|
||||||
|
n.links.loc[n.links.carrier == "DC", lk_attrs]
|
||||||
|
]).fillna(0)
|
||||||
|
|
||||||
|
positive_order = candidates.bus0 < candidates.bus1
|
||||||
|
candidates_p = candidates[positive_order]
|
||||||
|
swap_buses = {"bus0": "bus1", "bus1": "bus0"}
|
||||||
|
candidates_n = candidates[~positive_order].rename(columns=swap_buses)
|
||||||
|
candidates = pd.concat([candidates_p, candidates_n])
|
||||||
|
|
||||||
|
def make_index(c):
|
||||||
|
return prefix + c.bus0 + connector + c.bus1
|
||||||
|
|
||||||
|
topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean()
|
||||||
|
topo.index = topo.apply(make_index, axis=1)
|
||||||
|
|
||||||
|
if not bidirectional:
|
||||||
|
topo_reverse = topo.copy()
|
||||||
|
topo_reverse.rename(columns=swap_buses, inplace=True)
|
||||||
|
topo_reverse.index = topo_reverse.apply(make_index, axis=1)
|
||||||
|
topo = topo.append(topo_reverse)
|
||||||
|
|
||||||
|
return topo
|
||||||
|
|
||||||
|
|
||||||
# TODO merge issue with PyPSA-Eur
|
# TODO merge issue with PyPSA-Eur
|
||||||
def update_wind_solar_costs(n, costs):
|
def update_wind_solar_costs(n, costs):
|
||||||
"""
|
"""
|
||||||
@ -312,6 +431,9 @@ def patch_electricity_network(n):
|
|||||||
update_wind_solar_costs(n, costs)
|
update_wind_solar_costs(n, costs)
|
||||||
n.loads["carrier"] = "electricity"
|
n.loads["carrier"] = "electricity"
|
||||||
n.buses["location"] = n.buses.index
|
n.buses["location"] = n.buses.index
|
||||||
|
# remove trailing white space of load index until new PyPSA version after v0.18.
|
||||||
|
n.loads.rename(lambda x: x.strip(), inplace=True)
|
||||||
|
n.loads_t.p_set.rename(lambda x: x.strip(), axis=1, inplace=True)
|
||||||
|
|
||||||
|
|
||||||
def add_co2_tracking(n, options):
|
def add_co2_tracking(n, options):
|
||||||
@ -338,26 +460,26 @@ def add_co2_tracking(n, options):
|
|||||||
)
|
)
|
||||||
|
|
||||||
# this tracks CO2 stored, e.g. underground
|
# this tracks CO2 stored, e.g. underground
|
||||||
n.add("Bus",
|
n.madd("Bus",
|
||||||
"co2 stored",
|
spatial.co2.nodes,
|
||||||
location="EU",
|
location=spatial.co2.locations,
|
||||||
carrier="co2 stored"
|
carrier="co2 stored"
|
||||||
)
|
)
|
||||||
|
|
||||||
n.add("Store",
|
n.madd("Store",
|
||||||
"co2 stored",
|
spatial.co2.nodes,
|
||||||
e_nom_extendable=True,
|
e_nom_extendable=True,
|
||||||
e_nom_max=options['co2_sequestration_potential'] * 1e6,
|
e_nom_max=np.inf,
|
||||||
capital_cost=options['co2_sequestration_cost'],
|
capital_cost=options['co2_sequestration_cost'],
|
||||||
carrier="co2 stored",
|
carrier="co2 stored",
|
||||||
bus="co2 stored"
|
bus=spatial.co2.nodes
|
||||||
)
|
)
|
||||||
|
|
||||||
if options['co2_vent']:
|
if options['co2_vent']:
|
||||||
|
|
||||||
n.add("Link",
|
n.madd("Link",
|
||||||
"co2 vent",
|
spatial.co2.vents,
|
||||||
bus0="co2 stored",
|
bus0=spatial.co2.nodes,
|
||||||
bus1="co2 atmosphere",
|
bus1="co2 atmosphere",
|
||||||
carrier="co2 vent",
|
carrier="co2 vent",
|
||||||
efficiency=1.,
|
efficiency=1.,
|
||||||
@ -365,6 +487,28 @@ def add_co2_tracking(n, options):
|
|||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def add_co2_network(n, costs):
|
||||||
|
|
||||||
|
logger.info("Adding CO2 network.")
|
||||||
|
co2_links = create_network_topology(n, "CO2 pipeline ")
|
||||||
|
|
||||||
|
cost_onshore = (1 - co2_links.underwater_fraction) * costs.at['CO2 pipeline', 'fixed'] * co2_links.length
|
||||||
|
cost_submarine = co2_links.underwater_fraction * costs.at['CO2 submarine pipeline', 'fixed'] * co2_links.length
|
||||||
|
capital_cost = cost_onshore + cost_submarine
|
||||||
|
|
||||||
|
n.madd("Link",
|
||||||
|
co2_links.index,
|
||||||
|
bus0=co2_links.bus0.values + " co2 stored",
|
||||||
|
bus1=co2_links.bus1.values + " co2 stored",
|
||||||
|
p_min_pu=-1,
|
||||||
|
p_nom_extendable=True,
|
||||||
|
length=co2_links.length.values,
|
||||||
|
capital_cost=capital_cost.values,
|
||||||
|
carrier="CO2 pipeline",
|
||||||
|
lifetime=costs.at['CO2 pipeline', 'lifetime']
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
def add_dac(n, costs):
|
def add_dac(n, costs):
|
||||||
|
|
||||||
heat_carriers = ["urban central heat", "services urban decentral heat"]
|
heat_carriers = ["urban central heat", "services urban decentral heat"]
|
||||||
@ -375,10 +519,9 @@ def add_dac(n, costs):
|
|||||||
efficiency3 = -(costs.at['direct air capture', 'heat-input'] - costs.at['direct air capture', 'compression-heat-output'])
|
efficiency3 = -(costs.at['direct air capture', 'heat-input'] - costs.at['direct air capture', 'compression-heat-output'])
|
||||||
|
|
||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
locations,
|
heat_buses.str.replace(" heat", " DAC"),
|
||||||
suffix=" DAC",
|
|
||||||
bus0="co2 atmosphere",
|
bus0="co2 atmosphere",
|
||||||
bus1="co2 stored",
|
bus1=spatial.co2.df.loc[locations, "nodes"].values,
|
||||||
bus2=locations.values,
|
bus2=locations.values,
|
||||||
bus3=heat_buses,
|
bus3=heat_buses,
|
||||||
carrier="DAC",
|
carrier="DAC",
|
||||||
@ -522,6 +665,8 @@ def prepare_data(n):
|
|||||||
|
|
||||||
nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
|
nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
|
||||||
nodal_energy_totals.index = pop_layout.index
|
nodal_energy_totals.index = pop_layout.index
|
||||||
|
# district heat share not weighted by population
|
||||||
|
district_heat_share = nodal_energy_totals["district heat share"].round(2)
|
||||||
nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)
|
nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)
|
||||||
|
|
||||||
# copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
|
# copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
|
||||||
@ -644,7 +789,7 @@ def prepare_data(n):
|
|||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
return nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data
|
return nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share
|
||||||
|
|
||||||
|
|
||||||
# TODO checkout PyPSA-Eur script
|
# TODO checkout PyPSA-Eur script
|
||||||
@ -772,8 +917,9 @@ def insert_electricity_distribution_grid(n, costs):
|
|||||||
capital_cost=costs.at['electricity distribution grid', 'fixed'] * cost_factor
|
capital_cost=costs.at['electricity distribution grid', 'fixed'] * cost_factor
|
||||||
)
|
)
|
||||||
|
|
||||||
# this catches regular electricity load and "industry electricity"
|
# this catches regular electricity load and "industry electricity" and
|
||||||
loads = n.loads.index[n.loads.carrier.str.contains("electricity")]
|
# "agriculture machinery electric" and "agriculture electricity"
|
||||||
|
loads = n.loads.index[n.loads.carrier.str.contains("electric")]
|
||||||
n.loads.loc[loads, "bus"] += " low voltage"
|
n.loads.loc[loads, "bus"] += " low voltage"
|
||||||
|
|
||||||
bevs = n.links.index[n.links.carrier == "BEV charger"]
|
bevs = n.links.index[n.links.carrier == "BEV charger"]
|
||||||
@ -814,7 +960,8 @@ def insert_electricity_distribution_grid(n, costs):
|
|||||||
marginal_cost=n.generators.loc[solar, 'marginal_cost'],
|
marginal_cost=n.generators.loc[solar, 'marginal_cost'],
|
||||||
capital_cost=costs.at['solar-rooftop', 'fixed'],
|
capital_cost=costs.at['solar-rooftop', 'fixed'],
|
||||||
efficiency=n.generators.loc[solar, 'efficiency'],
|
efficiency=n.generators.loc[solar, 'efficiency'],
|
||||||
p_max_pu=n.generators_t.p_max_pu[solar]
|
p_max_pu=n.generators_t.p_max_pu[solar],
|
||||||
|
lifetime=costs.at['solar-rooftop', 'lifetime']
|
||||||
)
|
)
|
||||||
|
|
||||||
n.add("Carrier", "home battery")
|
n.add("Carrier", "home battery")
|
||||||
@ -947,7 +1094,7 @@ def add_storage(n, costs):
|
|||||||
)
|
)
|
||||||
|
|
||||||
# hydrogen stored overground (where not already underground)
|
# hydrogen stored overground (where not already underground)
|
||||||
h2_capital_cost = costs.at["hydrogen storage tank", "fixed"]
|
h2_capital_cost = costs.at["hydrogen storage tank incl. compressor", "fixed"]
|
||||||
nodes_overground = cavern_nodes.index.symmetric_difference(nodes)
|
nodes_overground = cavern_nodes.index.symmetric_difference(nodes)
|
||||||
|
|
||||||
n.madd("Store",
|
n.madd("Store",
|
||||||
@ -982,9 +1129,9 @@ def add_storage(n, costs):
|
|||||||
p_min_pu=-1,
|
p_min_pu=-1,
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
length=h2_links.length.values,
|
length=h2_links.length.values,
|
||||||
capital_cost=costs.at['H2 pipeline', 'fixed'] * h2_links.length.values,
|
capital_cost=costs.at['H2 (g) pipeline', 'fixed'] * h2_links.length.values,
|
||||||
carrier="H2 pipeline",
|
carrier="H2 pipeline",
|
||||||
lifetime=costs.at['H2 pipeline', 'lifetime']
|
lifetime=costs.at['H2 (g) pipeline', 'lifetime']
|
||||||
)
|
)
|
||||||
|
|
||||||
if options["gas_network"]:
|
if options["gas_network"]:
|
||||||
@ -1120,25 +1267,27 @@ def add_storage(n, costs):
|
|||||||
if options['methanation']:
|
if options['methanation']:
|
||||||
|
|
||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
nodes + " Sabatier",
|
spatial.nodes,
|
||||||
|
suffix=" Sabatier",
|
||||||
bus0=nodes + " H2",
|
bus0=nodes + " H2",
|
||||||
bus1=spatial.gas.nodes,
|
bus1=spatial.gas.nodes,
|
||||||
bus2="co2 stored",
|
bus2=spatial.co2.nodes,
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
carrier="Sabatier",
|
carrier="Sabatier",
|
||||||
efficiency=costs.at["methanation", "efficiency"],
|
efficiency=costs.at["methanation", "efficiency"],
|
||||||
efficiency2=-costs.at["methanation", "efficiency"] * costs.at['gas', 'CO2 intensity'],
|
efficiency2=-costs.at["methanation", "efficiency"] * costs.at['gas', 'CO2 intensity'],
|
||||||
capital_cost=costs.at["methanation", "fixed"],
|
capital_cost=costs.at["methanation", "fixed"] * costs.at["methanation", "efficiency"], # costs given per kW_gas
|
||||||
lifetime=costs.at['methanation', 'lifetime']
|
lifetime=costs.at['methanation', 'lifetime']
|
||||||
)
|
)
|
||||||
|
|
||||||
if options['helmeth']:
|
if options['helmeth']:
|
||||||
|
|
||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
nodes + " helmeth",
|
spatial.nodes,
|
||||||
|
suffix=" helmeth",
|
||||||
bus0=nodes,
|
bus0=nodes,
|
||||||
bus1=spatial.gas.nodes,
|
bus1=spatial.gas.nodes,
|
||||||
bus2="co2 stored",
|
bus2=spatial.co2.nodes,
|
||||||
carrier="helmeth",
|
carrier="helmeth",
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
efficiency=costs.at["helmeth", "efficiency"],
|
efficiency=costs.at["helmeth", "efficiency"],
|
||||||
@ -1151,11 +1300,12 @@ def add_storage(n, costs):
|
|||||||
if options['SMR']:
|
if options['SMR']:
|
||||||
|
|
||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
nodes + " SMR CC",
|
spatial.nodes,
|
||||||
|
suffix=" SMR CC",
|
||||||
bus0=spatial.gas.nodes,
|
bus0=spatial.gas.nodes,
|
||||||
bus1=nodes + " H2",
|
bus1=nodes + " H2",
|
||||||
bus2="co2 atmosphere",
|
bus2="co2 atmosphere",
|
||||||
bus3="co2 stored",
|
bus3=spatial.co2.nodes,
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
carrier="SMR CC",
|
carrier="SMR CC",
|
||||||
efficiency=costs.at["SMR CC", "efficiency"],
|
efficiency=costs.at["SMR CC", "efficiency"],
|
||||||
@ -1294,8 +1444,8 @@ def add_land_transport(n, costs):
|
|||||||
|
|
||||||
co2 = ice_share / ice_efficiency * transport[nodes].sum().sum() / 8760 * costs.at["oil", 'CO2 intensity']
|
co2 = ice_share / ice_efficiency * transport[nodes].sum().sum() / 8760 * costs.at["oil", 'CO2 intensity']
|
||||||
|
|
||||||
n.madd("Load",
|
n.add("Load",
|
||||||
["land transport oil emissions"],
|
"land transport oil emissions",
|
||||||
bus="co2 atmosphere",
|
bus="co2 atmosphere",
|
||||||
carrier="land transport oil emissions",
|
carrier="land transport oil emissions",
|
||||||
p_set=-co2
|
p_set=-co2
|
||||||
@ -1308,12 +1458,11 @@ def add_heat(n, costs):
|
|||||||
|
|
||||||
sectors = ["residential", "services"]
|
sectors = ["residential", "services"]
|
||||||
|
|
||||||
nodes = create_nodes_for_heat_sector()
|
|
||||||
|
nodes, dist_fraction, urban_fraction = create_nodes_for_heat_sector()
|
||||||
|
|
||||||
#NB: must add costs of central heating afterwards (EUR 400 / kWpeak, 50a, 1% FOM from Fraunhofer ISE)
|
#NB: must add costs of central heating afterwards (EUR 400 / kWpeak, 50a, 1% FOM from Fraunhofer ISE)
|
||||||
|
|
||||||
urban_fraction = options['central_fraction'] * pop_layout["urban"] / pop_layout[["urban", "rural"]].sum(axis=1)
|
|
||||||
|
|
||||||
# exogenously reduce space heat demand
|
# exogenously reduce space heat demand
|
||||||
if options["reduce_space_heat_exogenously"]:
|
if options["reduce_space_heat_exogenously"]:
|
||||||
dE = get(options["reduce_space_heat_exogenously_factor"], investment_year)
|
dE = get(options["reduce_space_heat_exogenously_factor"], investment_year)
|
||||||
@ -1344,15 +1493,22 @@ def add_heat(n, costs):
|
|||||||
## Add heat load
|
## Add heat load
|
||||||
|
|
||||||
for sector in sectors:
|
for sector in sectors:
|
||||||
|
# heat demand weighting
|
||||||
if "rural" in name:
|
if "rural" in name:
|
||||||
factor = 1 - urban_fraction[nodes[name]]
|
factor = 1 - urban_fraction[nodes[name]]
|
||||||
elif "urban" in name:
|
elif "urban central" in name:
|
||||||
factor = urban_fraction[nodes[name]]
|
factor = dist_fraction[nodes[name]]
|
||||||
|
elif "urban decentral" in name:
|
||||||
|
factor = urban_fraction[nodes[name]] - \
|
||||||
|
dist_fraction[nodes[name]]
|
||||||
|
else:
|
||||||
|
raise NotImplementedError(f" {name} not in " f"heat systems: {heat_systems}")
|
||||||
|
|
||||||
if sector in name:
|
if sector in name:
|
||||||
heat_load = heat_demand[[sector + " water",sector + " space"]].groupby(level=1,axis=1).sum()[nodes[name]].multiply(factor)
|
heat_load = heat_demand[[sector + " water",sector + " space"]].groupby(level=1,axis=1).sum()[nodes[name]].multiply(factor)
|
||||||
|
|
||||||
if name == "urban central":
|
if name == "urban central":
|
||||||
heat_load = heat_demand.groupby(level=1,axis=1).sum()[nodes[name]].multiply(urban_fraction[nodes[name]] * (1 + options['district_heating_loss']))
|
heat_load = heat_demand.groupby(level=1,axis=1).sum()[nodes[name]].multiply(factor * (1 + options['district_heating']['district_heating_loss']))
|
||||||
|
|
||||||
n.madd("Load",
|
n.madd("Load",
|
||||||
nodes[name],
|
nodes[name],
|
||||||
@ -1502,7 +1658,7 @@ def add_heat(n, costs):
|
|||||||
bus1=nodes[name],
|
bus1=nodes[name],
|
||||||
bus2=nodes[name] + " urban central heat",
|
bus2=nodes[name] + " urban central heat",
|
||||||
bus3="co2 atmosphere",
|
bus3="co2 atmosphere",
|
||||||
bus4="co2 stored",
|
bus4=spatial.co2.df.loc[nodes[name], "nodes"].values,
|
||||||
carrier="urban central gas CHP CC",
|
carrier="urban central gas CHP CC",
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
capital_cost=costs.at['central gas CHP', 'fixed']*costs.at['central gas CHP', 'efficiency'] + costs.at['biomass CHP capture', 'fixed']*costs.at['gas', 'CO2 intensity'],
|
capital_cost=costs.at['central gas CHP', 'fixed']*costs.at['central gas CHP', 'efficiency'] + costs.at['biomass CHP capture', 'fixed']*costs.at['gas', 'CO2 intensity'],
|
||||||
@ -1633,47 +1789,60 @@ def create_nodes_for_heat_sector():
|
|||||||
# urban are areas with high heating density
|
# urban are areas with high heating density
|
||||||
# urban can be split into district heating (central) and individual heating (decentral)
|
# urban can be split into district heating (central) and individual heating (decentral)
|
||||||
|
|
||||||
|
ct_urban = pop_layout.urban.groupby(pop_layout.ct).sum()
|
||||||
|
# distribution of urban population within a country
|
||||||
|
pop_layout["urban_ct_fraction"] = pop_layout.urban / pop_layout.ct.map(ct_urban.get)
|
||||||
|
|
||||||
sectors = ["residential", "services"]
|
sectors = ["residential", "services"]
|
||||||
|
|
||||||
nodes = {}
|
nodes = {}
|
||||||
|
urban_fraction = pop_layout.urban / pop_layout[["rural", "urban"]].sum(axis=1)
|
||||||
|
|
||||||
for sector in sectors:
|
for sector in sectors:
|
||||||
nodes[sector + " rural"] = pop_layout.index
|
nodes[sector + " rural"] = pop_layout.index
|
||||||
|
|
||||||
if options["central"]:
|
|
||||||
# TODO: this looks hardcoded, move to config
|
|
||||||
urban_decentral_ct = pd.Index(["ES", "GR", "PT", "IT", "BG"])
|
|
||||||
nodes[sector + " urban decentral"] = pop_layout.index[pop_layout.ct.isin(urban_decentral_ct)]
|
|
||||||
else:
|
|
||||||
nodes[sector + " urban decentral"] = pop_layout.index
|
nodes[sector + " urban decentral"] = pop_layout.index
|
||||||
|
|
||||||
# for central nodes, residential and services are aggregated
|
# maximum potential of urban demand covered by district heating
|
||||||
nodes["urban central"] = pop_layout.index.symmetric_difference(nodes["residential urban decentral"])
|
central_fraction = options['district_heating']["potential"]
|
||||||
|
# district heating share at each node
|
||||||
|
dist_fraction_node = district_heat_share * pop_layout["urban_ct_fraction"] / pop_layout["fraction"]
|
||||||
|
nodes["urban central"] = dist_fraction_node.index
|
||||||
|
# if district heating share larger than urban fraction -> set urban
|
||||||
|
# fraction to district heating share
|
||||||
|
urban_fraction = pd.concat([urban_fraction, dist_fraction_node],
|
||||||
|
axis=1).max(axis=1)
|
||||||
|
# difference of max potential and today's share of district heating
|
||||||
|
diff = (urban_fraction * central_fraction) - dist_fraction_node
|
||||||
|
progress = get(options["district_heating"]["progress"], investment_year)
|
||||||
|
dist_fraction_node += diff * progress
|
||||||
|
print(
|
||||||
|
"The current district heating share compared to the maximum",
|
||||||
|
f"possible is increased by a progress factor of\n{progress}",
|
||||||
|
f"resulting in a district heating share of\n{dist_fraction_node}"
|
||||||
|
)
|
||||||
|
|
||||||
return nodes
|
return nodes, dist_fraction_node, urban_fraction
|
||||||
|
|
||||||
|
|
||||||
def add_biomass(n, costs):
|
def add_biomass(n, costs):
|
||||||
|
|
||||||
print("adding biomass")
|
print("adding biomass")
|
||||||
|
|
||||||
# biomass distributed at country level - i.e. transport within country allowed
|
|
||||||
countries = n.buses.country.dropna().unique()
|
|
||||||
|
|
||||||
biomass_potentials = pd.read_csv(snakemake.input.biomass_potentials, index_col=0)
|
biomass_potentials = pd.read_csv(snakemake.input.biomass_potentials, index_col=0)
|
||||||
|
|
||||||
# potential per node distributed within country by population
|
|
||||||
biogas_pot = (biomass_potentials.loc[pop_layout.ct]
|
|
||||||
.set_index(pop_layout.index)
|
|
||||||
.mul(pop_layout.fraction, axis="index")
|
|
||||||
.rename(index=lambda x: x + " biogas")
|
|
||||||
)["biogas"]
|
|
||||||
|
|
||||||
# need to aggregate potentials if gas not nodally resolved
|
# need to aggregate potentials if gas not nodally resolved
|
||||||
if not options["gas_network"]:
|
if options["gas_network"]:
|
||||||
biogas_pot = biogas_pot.sum()
|
biogas_potentials_spatial = biomass_potentials["biogas"].rename(index=lambda x: x + " biogas")
|
||||||
|
else:
|
||||||
|
biogas_potentials_spatial = biomass_potentials["biogas"].sum()
|
||||||
|
|
||||||
|
if options["biomass_transport"]:
|
||||||
|
solid_biomass_potentials_spatial = biomass_potentials["solid biomass"].rename(index=lambda x: x + " solid biomass")
|
||||||
|
else:
|
||||||
|
solid_biomass_potentials_spatial = biomass_potentials["solid biomass"].sum()
|
||||||
|
|
||||||
|
|
||||||
n.add("Carrier", "biogas")
|
n.add("Carrier", "biogas")
|
||||||
|
|
||||||
n.add("Carrier", "solid biomass")
|
n.add("Carrier", "solid biomass")
|
||||||
|
|
||||||
n.madd("Bus",
|
n.madd("Bus",
|
||||||
@ -1682,9 +1851,9 @@ def add_biomass(n, costs):
|
|||||||
carrier="biogas"
|
carrier="biogas"
|
||||||
)
|
)
|
||||||
|
|
||||||
n.add("Bus",
|
n.madd("Bus",
|
||||||
"EU solid biomass",
|
spatial.biomass.nodes,
|
||||||
location="EU",
|
location=spatial.biomass.locations,
|
||||||
carrier="solid biomass"
|
carrier="solid biomass"
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -1692,18 +1861,18 @@ def add_biomass(n, costs):
|
|||||||
spatial.gas.biogas,
|
spatial.gas.biogas,
|
||||||
bus=spatial.gas.biogas,
|
bus=spatial.gas.biogas,
|
||||||
carrier="biogas",
|
carrier="biogas",
|
||||||
e_nom=biogas_pot,
|
e_nom=biogas_potentials_spatial,
|
||||||
marginal_cost=costs.at['biogas', 'fuel'],
|
marginal_cost=costs.at['biogas', 'fuel'],
|
||||||
e_initial=biogas_pot
|
e_initial=biogas_potentials_spatial
|
||||||
)
|
)
|
||||||
|
|
||||||
n.add("Store",
|
n.madd("Store",
|
||||||
"EU solid biomass",
|
spatial.biomass.nodes,
|
||||||
bus="EU solid biomass",
|
bus=spatial.biomass.nodes,
|
||||||
carrier="solid biomass",
|
carrier="solid biomass",
|
||||||
e_nom=biomass_potentials.loc[countries, "solid biomass"].sum(),
|
e_nom=solid_biomass_potentials_spatial,
|
||||||
marginal_cost=costs.at['solid biomass', 'fuel'],
|
marginal_cost=costs.at['solid biomass', 'fuel'],
|
||||||
e_initial=biomass_potentials.loc[countries, "solid biomass"].sum()
|
e_initial=solid_biomass_potentials_spatial
|
||||||
)
|
)
|
||||||
|
|
||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
@ -1718,6 +1887,32 @@ def add_biomass(n, costs):
|
|||||||
p_nom_extendable=True
|
p_nom_extendable=True
|
||||||
)
|
)
|
||||||
|
|
||||||
|
if options["biomass_transport"]:
|
||||||
|
|
||||||
|
transport_costs = pd.read_csv(
|
||||||
|
snakemake.input.biomass_transport_costs,
|
||||||
|
index_col=0,
|
||||||
|
squeeze=True
|
||||||
|
)
|
||||||
|
|
||||||
|
# add biomass transport
|
||||||
|
biomass_transport = create_network_topology(n, "biomass transport ", bidirectional=False)
|
||||||
|
|
||||||
|
# costs
|
||||||
|
bus0_costs = biomass_transport.bus0.apply(lambda x: transport_costs[x[:2]])
|
||||||
|
bus1_costs = biomass_transport.bus1.apply(lambda x: transport_costs[x[:2]])
|
||||||
|
biomass_transport["costs"] = pd.concat([bus0_costs, bus1_costs], axis=1).mean(axis=1)
|
||||||
|
|
||||||
|
n.madd("Link",
|
||||||
|
biomass_transport.index,
|
||||||
|
bus0=biomass_transport.bus0 + " solid biomass",
|
||||||
|
bus1=biomass_transport.bus1 + " solid biomass",
|
||||||
|
p_nom_extendable=True,
|
||||||
|
length=biomass_transport.length.values,
|
||||||
|
marginal_cost=biomass_transport.costs * biomass_transport.length.values,
|
||||||
|
capital_cost=1,
|
||||||
|
carrier="solid biomass transport"
|
||||||
|
)
|
||||||
|
|
||||||
#AC buses with district heating
|
#AC buses with district heating
|
||||||
urban_central = n.buses.index[n.buses.carrier == "urban central heat"]
|
urban_central = n.buses.index[n.buses.carrier == "urban central heat"]
|
||||||
@ -1728,7 +1923,7 @@ def add_biomass(n, costs):
|
|||||||
|
|
||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
urban_central + " urban central solid biomass CHP",
|
urban_central + " urban central solid biomass CHP",
|
||||||
bus0="EU solid biomass",
|
bus0=spatial.biomass.df.loc[urban_central, "nodes"].values,
|
||||||
bus1=urban_central,
|
bus1=urban_central,
|
||||||
bus2=urban_central + " urban central heat",
|
bus2=urban_central + " urban central heat",
|
||||||
carrier="urban central solid biomass CHP",
|
carrier="urban central solid biomass CHP",
|
||||||
@ -1742,11 +1937,11 @@ def add_biomass(n, costs):
|
|||||||
|
|
||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
urban_central + " urban central solid biomass CHP CC",
|
urban_central + " urban central solid biomass CHP CC",
|
||||||
bus0="EU solid biomass",
|
bus0=spatial.biomass.df.loc[urban_central, "nodes"].values,
|
||||||
bus1=urban_central,
|
bus1=urban_central,
|
||||||
bus2=urban_central + " urban central heat",
|
bus2=urban_central + " urban central heat",
|
||||||
bus3="co2 atmosphere",
|
bus3="co2 atmosphere",
|
||||||
bus4="co2 stored",
|
bus4=spatial.co2.df.loc[urban_central, "nodes"].values,
|
||||||
carrier="urban central solid biomass CHP CC",
|
carrier="urban central solid biomass CHP CC",
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
capital_cost=costs.at[key, 'fixed'] * costs.at[key, 'efficiency'] + costs.at['biomass CHP capture', 'fixed'] * costs.at['solid biomass', 'CO2 intensity'],
|
capital_cost=costs.at[key, 'fixed'] * costs.at[key, 'efficiency'] + costs.at['biomass CHP capture', 'fixed'] * costs.at['solid biomass', 'CO2 intensity'],
|
||||||
@ -1776,34 +1971,39 @@ def add_industry(n, costs):
|
|||||||
|
|
||||||
solid_biomass_by_country = industrial_demand["solid biomass"].groupby(pop_layout.ct).sum()
|
solid_biomass_by_country = industrial_demand["solid biomass"].groupby(pop_layout.ct).sum()
|
||||||
|
|
||||||
n.add("Bus",
|
n.madd("Bus",
|
||||||
"solid biomass for industry",
|
spatial.biomass.industry,
|
||||||
location="EU",
|
location=spatial.biomass.locations,
|
||||||
carrier="solid biomass for industry"
|
carrier="solid biomass for industry"
|
||||||
)
|
)
|
||||||
|
|
||||||
n.add("Load",
|
if options["biomass_transport"]:
|
||||||
"solid biomass for industry",
|
p_set = industrial_demand.loc[spatial.biomass.locations, "solid biomass"].rename(index=lambda x: x + " solid biomass for industry") / 8760
|
||||||
bus="solid biomass for industry",
|
else:
|
||||||
|
p_set = industrial_demand["solid biomass"].sum() / 8760
|
||||||
|
|
||||||
|
n.madd("Load",
|
||||||
|
spatial.biomass.industry,
|
||||||
|
bus=spatial.biomass.industry,
|
||||||
carrier="solid biomass for industry",
|
carrier="solid biomass for industry",
|
||||||
p_set=solid_biomass_by_country.sum() / 8760
|
p_set=p_set
|
||||||
)
|
)
|
||||||
|
|
||||||
n.add("Link",
|
n.madd("Link",
|
||||||
"solid biomass for industry",
|
spatial.biomass.industry,
|
||||||
bus0="EU solid biomass",
|
bus0=spatial.biomass.nodes,
|
||||||
bus1="solid biomass for industry",
|
bus1=spatial.biomass.industry,
|
||||||
carrier="solid biomass for industry",
|
carrier="solid biomass for industry",
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
efficiency=1.
|
efficiency=1.
|
||||||
)
|
)
|
||||||
|
|
||||||
n.add("Link",
|
n.madd("Link",
|
||||||
"solid biomass for industry CC",
|
spatial.biomass.industry_cc,
|
||||||
bus0="EU solid biomass",
|
bus0=spatial.biomass.nodes,
|
||||||
bus1="solid biomass for industry",
|
bus1=spatial.biomass.industry,
|
||||||
bus2="co2 atmosphere",
|
bus2="co2 atmosphere",
|
||||||
bus3="co2 stored",
|
bus3=spatial.co2.nodes,
|
||||||
carrier="solid biomass for industry CC",
|
carrier="solid biomass for industry CC",
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
capital_cost=costs.at["cement capture", "fixed"] * costs.at['solid biomass', 'CO2 intensity'],
|
capital_cost=costs.at["cement capture", "fixed"] * costs.at['solid biomass', 'CO2 intensity'],
|
||||||
@ -1841,7 +2041,7 @@ def add_industry(n, costs):
|
|||||||
bus0=spatial.gas.nodes,
|
bus0=spatial.gas.nodes,
|
||||||
bus1=spatial.gas.industry,
|
bus1=spatial.gas.industry,
|
||||||
bus2="co2 atmosphere",
|
bus2="co2 atmosphere",
|
||||||
bus3="co2 stored",
|
bus3=spatial.co2.nodes,
|
||||||
carrier="gas for industry CC",
|
carrier="gas for industry CC",
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
capital_cost=costs.at["cement capture", "fixed"] * costs.at['gas', 'CO2 intensity'],
|
capital_cost=costs.at["cement capture", "fixed"] * costs.at['gas', 'CO2 intensity'],
|
||||||
@ -1859,18 +2059,66 @@ def add_industry(n, costs):
|
|||||||
p_set=industrial_demand.loc[nodes, "hydrogen"] / 8760
|
p_set=industrial_demand.loc[nodes, "hydrogen"] / 8760
|
||||||
)
|
)
|
||||||
|
|
||||||
|
if options["shipping_hydrogen_liquefaction"]:
|
||||||
|
|
||||||
|
n.madd("Bus",
|
||||||
|
nodes,
|
||||||
|
suffix=" H2 liquid",
|
||||||
|
carrier="H2 liquid",
|
||||||
|
location=nodes
|
||||||
|
)
|
||||||
|
|
||||||
|
n.madd("Link",
|
||||||
|
nodes + " H2 liquefaction",
|
||||||
|
bus0=nodes + " H2",
|
||||||
|
bus1=nodes + " H2 liquid",
|
||||||
|
carrier="H2 liquefaction",
|
||||||
|
efficiency=costs.at["H2 liquefaction", 'efficiency'],
|
||||||
|
capital_cost=costs.at["H2 liquefaction", 'fixed'],
|
||||||
|
p_nom_extendable=True,
|
||||||
|
lifetime=costs.at['H2 liquefaction', 'lifetime']
|
||||||
|
)
|
||||||
|
|
||||||
|
shipping_bus = nodes + " H2 liquid"
|
||||||
|
else:
|
||||||
|
shipping_bus = nodes + " H2"
|
||||||
|
|
||||||
all_navigation = ["total international navigation", "total domestic navigation"]
|
all_navigation = ["total international navigation", "total domestic navigation"]
|
||||||
efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
|
efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
|
||||||
p_set = nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
|
shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
|
||||||
|
p_set = shipping_hydrogen_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
|
||||||
|
|
||||||
n.madd("Load",
|
n.madd("Load",
|
||||||
nodes,
|
nodes,
|
||||||
suffix=" H2 for shipping",
|
suffix=" H2 for shipping",
|
||||||
bus=nodes + " H2",
|
bus=shipping_bus,
|
||||||
carrier="H2 for shipping",
|
carrier="H2 for shipping",
|
||||||
p_set=p_set
|
p_set=p_set
|
||||||
)
|
)
|
||||||
|
|
||||||
|
if shipping_hydrogen_share < 1:
|
||||||
|
|
||||||
|
shipping_oil_share = 1 - shipping_hydrogen_share
|
||||||
|
|
||||||
|
p_set = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.
|
||||||
|
|
||||||
|
n.madd("Load",
|
||||||
|
nodes,
|
||||||
|
suffix=" shipping oil",
|
||||||
|
bus="EU oil",
|
||||||
|
carrier="shipping oil",
|
||||||
|
p_set=p_set
|
||||||
|
)
|
||||||
|
|
||||||
|
co2 = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
|
||||||
|
|
||||||
|
n.add("Load",
|
||||||
|
"shipping oil emissions",
|
||||||
|
bus="co2 atmosphere",
|
||||||
|
carrier="shipping oil emissions",
|
||||||
|
p_set=-co2
|
||||||
|
)
|
||||||
|
|
||||||
if "EU oil" not in n.buses.index:
|
if "EU oil" not in n.buses.index:
|
||||||
|
|
||||||
n.add("Bus",
|
n.add("Bus",
|
||||||
@ -1902,7 +2150,7 @@ def add_industry(n, costs):
|
|||||||
|
|
||||||
if options["oil_boilers"]:
|
if options["oil_boilers"]:
|
||||||
|
|
||||||
nodes_heat = create_nodes_for_heat_sector()
|
nodes_heat = create_nodes_for_heat_sector()[0]
|
||||||
|
|
||||||
for name in ["residential rural", "services rural", "residential urban decentral", "services urban decentral"]:
|
for name in ["residential rural", "services rural", "residential urban decentral", "services urban decentral"]:
|
||||||
|
|
||||||
@ -1923,7 +2171,7 @@ def add_industry(n, costs):
|
|||||||
nodes + " Fischer-Tropsch",
|
nodes + " Fischer-Tropsch",
|
||||||
bus0=nodes + " H2",
|
bus0=nodes + " H2",
|
||||||
bus1="EU oil",
|
bus1="EU oil",
|
||||||
bus2="co2 stored",
|
bus2=spatial.co2.nodes,
|
||||||
carrier="Fischer-Tropsch",
|
carrier="Fischer-Tropsch",
|
||||||
efficiency=costs.at["Fischer-Tropsch", 'efficiency'],
|
efficiency=costs.at["Fischer-Tropsch", 'efficiency'],
|
||||||
capital_cost=costs.at["Fischer-Tropsch", 'fixed'],
|
capital_cost=costs.at["Fischer-Tropsch", 'fixed'],
|
||||||
@ -2012,11 +2260,12 @@ def add_industry(n, costs):
|
|||||||
)
|
)
|
||||||
|
|
||||||
#assume enough local waste heat for CC
|
#assume enough local waste heat for CC
|
||||||
n.add("Link",
|
n.madd("Link",
|
||||||
"process emissions CC",
|
spatial.co2.locations,
|
||||||
|
suffix=" process emissions CC",
|
||||||
bus0="process emissions",
|
bus0="process emissions",
|
||||||
bus1="co2 atmosphere",
|
bus1="co2 atmosphere",
|
||||||
bus2="co2 stored",
|
bus2=spatial.co2.nodes,
|
||||||
carrier="process emissions CC",
|
carrier="process emissions CC",
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
capital_cost=costs.at["cement capture", "fixed"],
|
capital_cost=costs.at["cement capture", "fixed"],
|
||||||
@ -2046,6 +2295,71 @@ def add_waste_heat(n):
|
|||||||
n.links.loc[urban_central + " H2 Fuel Cell", "efficiency2"] = 0.95 - n.links.loc[urban_central + " H2 Fuel Cell", "efficiency"]
|
n.links.loc[urban_central + " H2 Fuel Cell", "efficiency2"] = 0.95 - n.links.loc[urban_central + " H2 Fuel Cell", "efficiency"]
|
||||||
|
|
||||||
|
|
||||||
|
def add_agriculture(n, costs):
|
||||||
|
|
||||||
|
logger.info('Add agriculture, forestry and fishing sector.')
|
||||||
|
|
||||||
|
nodes = pop_layout.index
|
||||||
|
|
||||||
|
# electricity
|
||||||
|
|
||||||
|
n.madd("Load",
|
||||||
|
nodes,
|
||||||
|
suffix=" agriculture electricity",
|
||||||
|
bus=nodes,
|
||||||
|
carrier='agriculture electricity',
|
||||||
|
p_set=nodal_energy_totals.loc[nodes, "total agriculture electricity"] * 1e6 / 8760
|
||||||
|
)
|
||||||
|
|
||||||
|
# heat
|
||||||
|
|
||||||
|
n.madd("Load",
|
||||||
|
nodes,
|
||||||
|
suffix=" agriculture heat",
|
||||||
|
bus=nodes + " services rural heat",
|
||||||
|
carrier="agriculture heat",
|
||||||
|
p_set=nodal_energy_totals.loc[nodes, "total agriculture heat"] * 1e6 / 8760
|
||||||
|
)
|
||||||
|
|
||||||
|
# machinery
|
||||||
|
|
||||||
|
electric_share = get(options["agriculture_machinery_electric_share"], investment_year)
|
||||||
|
assert electric_share <= 1.
|
||||||
|
ice_share = 1 - electric_share
|
||||||
|
|
||||||
|
machinery_nodal_energy = nodal_energy_totals.loc[nodes, "total agriculture machinery"]
|
||||||
|
|
||||||
|
if electric_share > 0:
|
||||||
|
|
||||||
|
efficiency_gain = options["agriculture_machinery_fuel_efficiency"] / options["agriculture_machinery_electric_efficiency"]
|
||||||
|
|
||||||
|
n.madd("Load",
|
||||||
|
nodes,
|
||||||
|
suffix=" agriculture machinery electric",
|
||||||
|
bus=nodes,
|
||||||
|
carrier="agriculture machinery electric",
|
||||||
|
p_set=electric_share / efficiency_gain * machinery_nodal_energy * 1e6 / 8760,
|
||||||
|
)
|
||||||
|
|
||||||
|
if ice_share > 0:
|
||||||
|
|
||||||
|
n.add("Load",
|
||||||
|
"agriculture machinery oil",
|
||||||
|
bus="EU oil",
|
||||||
|
carrier="agriculture machinery oil",
|
||||||
|
p_set=ice_share * machinery_nodal_energy.sum() * 1e6 / 8760
|
||||||
|
)
|
||||||
|
|
||||||
|
co2 = ice_share * machinery_nodal_energy.sum() * 1e6 / 8760 * costs.at["oil", 'CO2 intensity']
|
||||||
|
|
||||||
|
n.add("Load",
|
||||||
|
"agriculture machinery oil emissions",
|
||||||
|
bus="co2 atmosphere",
|
||||||
|
carrier="agriculture machinery oil emissions",
|
||||||
|
p_set=-co2
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
def decentral(n):
|
def decentral(n):
|
||||||
"""Removes the electricity transmission system."""
|
"""Removes the electricity transmission system."""
|
||||||
n.lines.drop(n.lines.index, inplace=True)
|
n.lines.drop(n.lines.index, inplace=True)
|
||||||
@ -2070,14 +2384,19 @@ def maybe_adjust_costs_and_potentials(n, opts):
|
|||||||
suptechs = map(lambda c: c.split("-", 2)[0], carrier_list)
|
suptechs = map(lambda c: c.split("-", 2)[0], carrier_list)
|
||||||
if oo[0].startswith(tuple(suptechs)):
|
if oo[0].startswith(tuple(suptechs)):
|
||||||
carrier = oo[0]
|
carrier = oo[0]
|
||||||
attr_lookup = {"p": "p_nom_max", "c": "capital_cost"}
|
attr_lookup = {"p": "p_nom_max", "e": "e_nom_max", "c": "capital_cost"}
|
||||||
attr = attr_lookup[oo[1][0]]
|
attr = attr_lookup[oo[1][0]]
|
||||||
factor = float(oo[1][1:])
|
factor = float(oo[1][1:])
|
||||||
#beware if factor is 0 and p_nom_max is np.inf, 0*np.inf is nan
|
#beware if factor is 0 and p_nom_max is np.inf, 0*np.inf is nan
|
||||||
if carrier == "AC": # lines do not have carrier
|
if carrier == "AC": # lines do not have carrier
|
||||||
n.lines[attr] *= factor
|
n.lines[attr] *= factor
|
||||||
else:
|
else:
|
||||||
comps = {"Generator", "Link", "StorageUnit"} if attr == 'p_nom_max' else {"Generator", "Link", "StorageUnit", "Store"}
|
if attr == 'p_nom_max':
|
||||||
|
comps = {"Generator", "Link", "StorageUnit"}
|
||||||
|
elif attr == 'e_nom_max':
|
||||||
|
comps = {"Store"}
|
||||||
|
else:
|
||||||
|
comps = {"Generator", "Link", "StorageUnit", "Store"}
|
||||||
for c in n.iterate_components(comps):
|
for c in n.iterate_components(comps):
|
||||||
if carrier=='solar':
|
if carrier=='solar':
|
||||||
sel = c.df.carrier.str.contains(carrier) & ~c.df.carrier.str.contains("solar rooftop")
|
sel = c.df.carrier.str.contains(carrier) & ~c.df.carrier.str.contains("solar rooftop")
|
||||||
@ -2094,17 +2413,18 @@ def limit_individual_line_extension(n, maxext):
|
|||||||
hvdc = n.links.index[n.links.carrier == 'DC']
|
hvdc = n.links.index[n.links.carrier == 'DC']
|
||||||
n.links.loc[hvdc, 'p_nom_max'] = n.links.loc[hvdc, 'p_nom'] + maxext
|
n.links.loc[hvdc, 'p_nom_max'] = n.links.loc[hvdc, 'p_nom'] + maxext
|
||||||
|
|
||||||
|
#%%
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
if 'snakemake' not in globals():
|
if 'snakemake' not in globals():
|
||||||
from helper import mock_snakemake
|
from helper import mock_snakemake
|
||||||
snakemake = mock_snakemake(
|
snakemake = mock_snakemake(
|
||||||
'prepare_sector_network',
|
'prepare_sector_network',
|
||||||
simpl='',
|
simpl='',
|
||||||
clusters=48,
|
opts="",
|
||||||
|
clusters="37",
|
||||||
lv=1.0,
|
lv=1.0,
|
||||||
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
|
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
|
||||||
planning_horizons=2020,
|
planning_horizons="2020",
|
||||||
)
|
)
|
||||||
|
|
||||||
logging.basicConfig(level=snakemake.config['logging_level'])
|
logging.basicConfig(level=snakemake.config['logging_level'])
|
||||||
@ -2129,6 +2449,8 @@ if __name__ == "__main__":
|
|||||||
|
|
||||||
patch_electricity_network(n)
|
patch_electricity_network(n)
|
||||||
|
|
||||||
|
define_spatial(pop_layout.index)
|
||||||
|
|
||||||
if snakemake.config["foresight"] == 'myopic':
|
if snakemake.config["foresight"] == 'myopic':
|
||||||
|
|
||||||
add_lifetime_wind_solar(n, costs)
|
add_lifetime_wind_solar(n, costs)
|
||||||
@ -2152,11 +2474,13 @@ if __name__ == "__main__":
|
|||||||
if o[:4] == "dist":
|
if o[:4] == "dist":
|
||||||
options['electricity_distribution_grid'] = True
|
options['electricity_distribution_grid'] = True
|
||||||
options['electricity_distribution_grid_cost_factor'] = float(o[4:].replace("p", ".").replace("m", "-"))
|
options['electricity_distribution_grid_cost_factor'] = float(o[4:].replace("p", ".").replace("m", "-"))
|
||||||
|
if o == "biomasstransport":
|
||||||
|
options["biomass_transport"] = True
|
||||||
|
|
||||||
nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data = prepare_data(n)
|
nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share = prepare_data(n)
|
||||||
|
|
||||||
if "nodistrict" in opts:
|
if "nodistrict" in opts:
|
||||||
options["central"] = False
|
options["district_heating"]["progress"] = 0.0
|
||||||
|
|
||||||
if "T" in opts:
|
if "T" in opts:
|
||||||
add_land_transport(n, costs)
|
add_land_transport(n, costs)
|
||||||
@ -2173,6 +2497,9 @@ if __name__ == "__main__":
|
|||||||
if "I" in opts and "H" in opts:
|
if "I" in opts and "H" in opts:
|
||||||
add_waste_heat(n)
|
add_waste_heat(n)
|
||||||
|
|
||||||
|
if "A" in opts: # requires H and I
|
||||||
|
add_agriculture(n, costs)
|
||||||
|
|
||||||
if options['dac']:
|
if options['dac']:
|
||||||
add_dac(n, costs)
|
add_dac(n, costs)
|
||||||
|
|
||||||
@ -2182,6 +2509,9 @@ if __name__ == "__main__":
|
|||||||
if "noH2network" in opts:
|
if "noH2network" in opts:
|
||||||
remove_h2_network(n)
|
remove_h2_network(n)
|
||||||
|
|
||||||
|
if options["co2_network"]:
|
||||||
|
add_co2_network(n, costs)
|
||||||
|
|
||||||
for o in opts:
|
for o in opts:
|
||||||
m = re.match(r'^\d+h$', o, re.IGNORECASE)
|
m = re.match(r'^\d+h$', o, re.IGNORECASE)
|
||||||
if m is not None:
|
if m is not None:
|
||||||
|
|||||||
@ -3,6 +3,7 @@
|
|||||||
import pypsa
|
import pypsa
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
from pypsa.linopt import get_var, linexpr, define_constraints
|
from pypsa.linopt import get_var, linexpr, define_constraints
|
||||||
|
|
||||||
@ -19,7 +20,15 @@ pypsa.pf.logger.setLevel(logging.WARNING)
|
|||||||
|
|
||||||
def add_land_use_constraint(n):
|
def add_land_use_constraint(n):
|
||||||
|
|
||||||
|
if 'm' in snakemake.wildcards.clusters:
|
||||||
|
_add_land_use_constraint_m(n)
|
||||||
|
else:
|
||||||
|
_add_land_use_constraint(n)
|
||||||
|
|
||||||
|
|
||||||
|
def _add_land_use_constraint(n):
|
||||||
#warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'
|
#warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'
|
||||||
|
|
||||||
for carrier in ['solar', 'onwind', 'offwind-ac', 'offwind-dc']:
|
for carrier in ['solar', 'onwind', 'offwind-ac', 'offwind-dc']:
|
||||||
existing = n.generators.loc[n.generators.carrier==carrier,"p_nom"].groupby(n.generators.bus.map(n.buses.location)).sum()
|
existing = n.generators.loc[n.generators.carrier==carrier,"p_nom"].groupby(n.generators.bus.map(n.buses.location)).sum()
|
||||||
existing.index += " " + carrier + "-" + snakemake.wildcards.planning_horizons
|
existing.index += " " + carrier + "-" + snakemake.wildcards.planning_horizons
|
||||||
@ -28,6 +37,33 @@ def add_land_use_constraint(n):
|
|||||||
n.generators.p_nom_max.clip(lower=0, inplace=True)
|
n.generators.p_nom_max.clip(lower=0, inplace=True)
|
||||||
|
|
||||||
|
|
||||||
|
def _add_land_use_constraint_m(n):
|
||||||
|
# if generators clustering is lower than network clustering, land_use accounting is at generators clusters
|
||||||
|
|
||||||
|
planning_horizons = snakemake.config["scenario"]["planning_horizons"]
|
||||||
|
grouping_years = snakemake.config["existing_capacities"]["grouping_years"]
|
||||||
|
current_horizon = snakemake.wildcards.planning_horizons
|
||||||
|
|
||||||
|
for carrier in ['solar', 'onwind', 'offwind-ac', 'offwind-dc']:
|
||||||
|
|
||||||
|
existing = n.generators.loc[n.generators.carrier==carrier,"p_nom"]
|
||||||
|
ind = list(set([i.split(sep=" ")[0] + ' ' + i.split(sep=" ")[1] for i in existing.index]))
|
||||||
|
|
||||||
|
previous_years = [
|
||||||
|
str(y) for y in
|
||||||
|
planning_horizons + grouping_years
|
||||||
|
if y < int(snakemake.wildcards.planning_horizons)
|
||||||
|
]
|
||||||
|
|
||||||
|
for p_year in previous_years:
|
||||||
|
ind2 = [i for i in ind if i + " " + carrier + "-" + p_year in existing.index]
|
||||||
|
sel_current = [i + " " + carrier + "-" + current_horizon for i in ind2]
|
||||||
|
sel_p_year = [i + " " + carrier + "-" + p_year for i in ind2]
|
||||||
|
n.generators.loc[sel_current, "p_nom_max"] -= existing.loc[sel_p_year].rename(lambda x: x[:-4] + current_horizon)
|
||||||
|
|
||||||
|
n.generators.p_nom_max.clip(lower=0, inplace=True)
|
||||||
|
|
||||||
|
|
||||||
def prepare_network(n, solve_opts=None):
|
def prepare_network(n, solve_opts=None):
|
||||||
|
|
||||||
if 'clip_p_max_pu' in solve_opts:
|
if 'clip_p_max_pu' in solve_opts:
|
||||||
@ -150,7 +186,6 @@ def add_chp_constraints(n):
|
|||||||
define_constraints(n, lhs, "<=", 0, 'chplink', 'backpressure')
|
define_constraints(n, lhs, "<=", 0, 'chplink', 'backpressure')
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def add_pipe_retrofit_constraint(n):
|
def add_pipe_retrofit_constraint(n):
|
||||||
"""Add constraint for retrofitting existing CH4 pipelines to H2 pipelines."""
|
"""Add constraint for retrofitting existing CH4 pipelines to H2 pipelines."""
|
||||||
|
|
||||||
@ -173,10 +208,27 @@ def add_pipe_retrofit_constraint(n):
|
|||||||
define_constraints(n, lhs, "=", pipe_capacity, 'Link', 'pipe_retrofit')
|
define_constraints(n, lhs, "=", pipe_capacity, 'Link', 'pipe_retrofit')
|
||||||
|
|
||||||
|
|
||||||
|
def add_co2_sequestration_limit(n, sns):
|
||||||
|
|
||||||
|
co2_stores = n.stores.loc[n.stores.carrier=='co2 stored'].index
|
||||||
|
|
||||||
|
if co2_stores.empty or ('Store', 'e') not in n.variables.index:
|
||||||
|
return
|
||||||
|
|
||||||
|
vars_final_co2_stored = get_var(n, 'Store', 'e').loc[sns[-1], co2_stores]
|
||||||
|
|
||||||
|
lhs = linexpr((1, vars_final_co2_stored)).sum()
|
||||||
|
rhs = n.config["sector"].get("co2_sequestration_potential", 200) * 1e6
|
||||||
|
|
||||||
|
name = 'co2_sequestration_limit'
|
||||||
|
define_constraints(n, lhs, "<=", rhs, 'GlobalConstraint',
|
||||||
|
'mu', axes=pd.Index([name]), spec=name)
|
||||||
|
|
||||||
|
|
||||||
def extra_functionality(n, snapshots):
|
def extra_functionality(n, snapshots):
|
||||||
add_chp_constraints(n)
|
|
||||||
add_battery_constraints(n)
|
add_battery_constraints(n)
|
||||||
add_pipe_retrofit_constraint(n)
|
add_pipe_retrofit_constraint(n)
|
||||||
|
add_co2_sequestration_limit(n, snapshots)
|
||||||
|
|
||||||
|
|
||||||
def solve_network(n, config, opts='', **kwargs):
|
def solve_network(n, config, opts='', **kwargs):
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user